On the convergence of adaptive sequential Monte Carlo methods

In several implementations of Sequential Monte Carlo (SMC) methods it is natural and important, in terms of algorithmic efficiency, to exploit the information of the history of the samples to optimally tune their subsequent propagations. In this article we provide a carefully formulated asymptotic theory for a class of such adaptive SMC methods. The theoretical framework developed here will cover, under assumptions, several commonly used SMC algorithms [Chopin, Biometrika 89 (2002) 539–551; Jasra et al., Scand. J. Stat. 38 (2011) 1–22; Schäfer and Chopin, Stat. Comput. 23 (2013) 163–184]. There are only limited results about the theoretical underpinning of such adaptive methods: we will bridge this gap by providing a weak law of large numbers (WLLN) and a central limit theorem (CLT) for some of these algorithms. The latter seems to be the first result of its kind in the literature and provides a formal justification of algorithms used in many real data contexts [Jasra et al. (2011); Schäfer and Chopin (2013)]. We establish that for a general class of adaptive SMC algorithms [Chopin (2002)], the asymptotic variance of the estimators from the adaptive SMC method is identical to a “limiting” SMC algorithm which uses ideal proposal kernels. Our results are supported by application on a complex high-dimensional posterior distribution associated with the Navier–Stokes model, where adapting high-dimensional parameters of the proposal kernels is critical for the efficiency of the algorithm

A variable speed of sound formulation for weakly compressible smoothed particle hydrodynamics

We present a Weakly Compressible SPH (WCSPH) formulation with a temporally variable speed of sound. The benefits of a time-varying sound speed formulation and the weaknesses of a constant sound speed formulation are worked out. It is shown how a variable sound speed can improve the performance, accuracy, and applicability of the WCSPH method. In our novel Uniform Compressible SPH (UCSPH) method, the required artificial speed of sound is calculated at each time step based on the current flow field. The method's robustness, performance, and accuracy are demonstrated with three test cases: a Taylor-Green vortex flow, a falling droplet impact, and a dam break. For all showcases, we observe at least similar accuracy as computed with WCSPH at strongly improved computational performance

Evaluation of the presence of the bap gene in Staphylococcus aureus isolates recovered from human and animals species.

International audienceThe implication of biofilm in chronic bacterial infection in many species has triggered an increasing interest in the characterization of genes involved in biofilm formation. The bap gene is a newly identified gene that encodes the biofilm-associated protein, BAP, which is involved in biofilm formation in Staphylococcus aureus. So far the bap gene has only been found in a small proportion of S. aureus strains from bovine mastitis in Spain. In order to study the presence of the bap gene in S. aureus isolates obtained from other species and various locations, a collection of 262 isolates was tested by PCR, using published primers and dot-blot. The results indicated that none of the isolates carried the bap gene suggesting that the prevalence of this gene among S. aureus isolates should be very low

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

The dental lamina: an essential structure for perpetual tooth regeneration in sharks

In recent years non-classical models have emerged as mainstays for studies of evolutionary, developmental and regenerative biology. Genomic advances have promoted the use of alternative taxa for the study of developmental biology, and the shark is one such emerging model vertebrate. Our research utilizes the embryonic shark (Scyliorhinus canicula) to characterize key developmental and regenerative processes that have been overlooked or not possible to study with more classic developmental models. Tooth development is a major event in the construction of the vertebrate body plan, linked in part with the emergence of jaws. Early development of the teeth and morphogenesis is well known from the murine model, but the process of tooth redevelopment and regeneration is less well known. Here we explore the role of the dental lamina in the development of a highly regenerative dentition in sharks. The shark represents a polyphyodont vertebrate with continuously repeated whole tooth regeneration. This is presented as a major developmental shift from the more derived renewal process that the murine model offers, where incisors exhibit continuous renewal and growth of the same tooth. Not only does the shark offer a study system for whole unit dental regeneration, it also represents an important model for understanding the evolutionary context of vertebrate tooth regeneration