Adaptive Priors based on Splines with Random Knots

Splines are useful building blocks when constructing priors on nonparametric models indexed by functions. Recently it has been established in the literature that hierarchical priors based on splines with a random number of equally spaced knots and random coefficients in the B-spline basis corresponding to those knots lead, under certain conditions, to adaptive posterior contraction rates, over certain smoothness functional classes. In this paper we extend these results for when the location of the knots is also endowed with a prior. This has already been a common practice in MCMC applications, where the resulting posterior is expected to be more "spatially adaptive", but a theoretical basis in terms of adaptive contraction rates was missing. Under some mild assumptions, we establish a result that provides sufficient conditions for adaptive contraction rates in a range of models

Rate-optimal Bayesian intensity smoothing for inhomogeneous Poisson processes

We apply nonparametric Bayesian methods to study the problem of estimating the intensity function of an inhomogeneous Poisson process. We exhibit a prior on intensities which both leads to a computationally feasible method and enjoys desirable theoretical optimality properties. The prior we use is based on B-spline expansions with free knots, adapted from well-established methods used in regression, for instance. We illustrate its practical use in the Poisson process setting by analyzing count data coming from a call centre. Theoretically we derive a new general theorem on contraction rates for posteriors in the setting of intensity function estimation. Practical choices that have to be made in the construction of our concrete prior, such as choosing the priors on the number and the locations of the spline knots, are based on these theoretical findings. The results assert that when properly constructed, our approach yields a rate-optimal procedure that automatically adapts to the regularity of the unknown intensity function

Ribonucleotide reductases: essential enzymes for bacterial life

Ribonucleotide reductase (RNR) is a key enzyme that mediates the synthesis of deoxyribonucleotides, the DNA precursors, for DNA synthesis in every living cell. This enzyme converts ribonucleotides to deoxyribonucleotides, the building blocks for DNA replication, and repair. Clearly, RNR enzymes have contributed to the appearance of genetic material that exists today, being essential for the evolution of all organisms on Earth. The strict control of RNR activity and dNTP pool sizes is important, as pool imbalances increase mutation rates, replication anomalies, and genome instability. Thus, RNR activity should be finely regulated allosterically and at the transcriptional level. In this review we examine the distribution, the evolution, and the genetic regulation of bacterial RNRs. Moreover, this enzyme can be considered an ideal target for anti-proliferative compounds designed to inhibit cell replication in eukaryotic cells (cancer cells), parasites, viruses, and bacteria

Increasing power-law range in avalanche amplitude and energy distributions

Power-law type probability density functions spanning several orders of magnitude are found for different avalanche properties. We propose a methodology to overcome empirical constrains that limit the power-law range for the distributions of different avalanche observables like amplitude, energy, duration or size. By considering catalogs of events that cover different observation windows, maximum likelihood estimation of a global power-law exponent is computed. This methodology is applied to amplitude and energy distributions of acoustic emission avalanches in failure-under- compression experiments of a nanoporous silica glass, finding in some cases global exponents in an unprecedented broad range: 4.5 decades for amplitudes and 9.5 decades for energies. In the later case, however, strict statistical analysis suggests experimental limitations might alter the power-law behavior.Comment: 23 pages, 7 figure

High time resolution and high signal-to-noise monitoring of the bacterial growth kinetics in the presence of plasmonic nanoparticles

Background: Emerging concepts for designing innovative drugs (i.e., novel generations of antimicrobials) frequently include nanostructures, new materials, and nanoparticles (NPs). Along with numerous advantages, NPs bring limitations, partly because they can limit the analytical techniques used for their biological and in vivo validation. From that standpoint, designing innovative drug delivery systems requires advancements in the methods used for their testing and investigations. Considering the well-known ability of resazurin-based methods for rapid detection of bacterial metabolisms with very high sensitivity, in this work we report a novel optimization for tracking bacterial growth kinetics in the presence of NPs with specific characteristics, such as specific optical properties. Results: Arginine-functionalized gold composite (HAp/Au/arginine) NPs, used as the NP model for validation of the method, possess plasmonic properties and are characterized by intensive absorption in the UV/vis region with a surface plasmon resonance maximum at 540 nm. Due to the specific optical properties, the NP absorption intensively interferes with the light absorption measured during the evaluation of bacterial growth (optical density; OD600). The results confirm substantial nonspecific interference by NPs in the signal detected during a regular turbidity study used for tracking bacterial growth. Instead, during application of a resazurin-based method (Presto Blue), when a combination of absorption and fluorescence detection is applied, a substantial increase in the signal-to-noise ratio is obtained that leads to the improvement of the accuracy of the measurements as verified in three bacterial strains tested with different growth rates (E. coli, P. aeruginosa, and S. aureus). Conclusions: Here, we described a novel procedure that enables the kinetics of bacterial growth in the presence of NPs to be followed with high time resolution, high sensitivity, and without sampling during the kinetic study. We showed the applicability of the Presto Blue method for the case of HAp/Au/arginine NPs, which can be extended to various types of metallic NPs with similar characteristics. The method is a very easy, economical, and reliable option for testing NPs designed as novel antimicrobials

Investigating bacterial infections in Galleria mellonella larvae: Insights into pathogen dissemination and behavior

The insect Galleria mellonella is an alternative animal model widely used for studying bacterial infections. It presents a wide range of advantages, including its low cost, easy maintenance and lack of ethical constraints. Among other features, their innate immune system is very similar to that of mammals. In this study, we dissected several larvae infected with important human pathogens: Mycobacterium abscessus, Staphylococcus aureus and Pseudomonas aeruginosa. By observing the fat body, gut, trachea, and hemolymph under the microscope, we were able to describe where bacteria tend to disseminate. We also quantified the number of bacteria in the hemolymph throughout the infection course and found significant differences between the different pathogens. With this work, we aimed to better understand the behavior and dissemination of bacteria in the infected larvae