Effect of Conventional Mouthrinses on Initial Bioadhesion to Enamel and Dentin in situ

Aim: The study aimed to investigate the effect of a customary fluoride solution, containing sodium fluoride and amine fluoride, on initial biofilm formation on enamel and dentin in situ compared directly to chlorhexidine. Methods: Bovine enamel and dentin specimens were mounted on maxillary splints carried by 9 subjects. After 1 min of pellicle formation, rinses with tap water (control), chlorhexidine (meridol med CHX 0.2%, GABA) and a fluoride mouthrinse (elmex, GABA) were performed for 1 min. Subsequently, the slabs were carried for another 8 h. The adherent bacteria were determined by DAPI staining, live-dead staining and determination of colony-forming units after desorption; glucan formation was visualized with concanavalin A. Additionally, energy-dispersive X-ray spectroscopy (EDX) analysis of the in situ biofilm layers was conducted, and contact angle measurements were performed. Statistical evaluation was performed by means of the Kruskal-Wallis test followed by the Mann-Whitney U test (p < 0.05). Results: In the control group, significantly higher amounts of adherent bacteria were detected on dentin (4.8 x 10⁶ ± 5.4 x 10⁶ bacteria/cm²) than on enamel (1.2 x 10⁶ ± 1.5 x 10⁶ bacteria/cm² , DAPI). Chlorhexidine significantly reduced the amount of adherent bacteria (dentin: 2.8 x 10⁵ ± 3.4 x 10⁵ bacteria/cm² ; enamel: 4.2 x 10⁵ ± 8.7 x 10⁵ bacteria/cm²). Rinses with the fluoride solution also significantly reduced bacterial adherence to dentin (8.1 x 10⁵ ± 1.5 x 10⁶ bacteria/cm²). Fluoride could not be detected by EDX analysis of the biofilms. Fluoride mouthrinsing did not influence the wettability of the pellicle-covered enamel surface. Conclusion: In addition to the reduction of demineralization and antibacterial effects, fluorides inhibit initial biofilm formation on dental hard tissues considerably, especially on dentin

Diffusion of peroxides through dentine in vitro with and without prior use of a desensitizing varnish

Different bleaching regimens are used in dentistry possibly penetrating the dentine and affecting the pulp. The aim of the present study was to investigate peroxide diffusion through dentine pre-treated with a desensitizing varnish (Vivasens®) in a standardized in vitro setup during application of different bleaching materials. The penetration was tested using 1.3-mm-thick bovine dentine slabs. The following bleaching materials were tested with and without prior application of the desensitizing varnish on the external side of the dentine slabs: Vivastyle, Whitestrips, Simply White, Opalescence (external bleaching), and sodium perborate (internal bleaching, only tested without varnish; n = 8 samples per subgroup). The penetration of peroxides was measured photometrically using 4-aminoantipyrin as a substrate, the penetration of peroxides was monitored over 240 min. All bleaching agents yielded a diffusion of peroxides through the dentine, the kinetics of penetration were approximately linear for all materials tested. The significantly highest diffusion of peroxides was observed with Opalescence, the lowest with sodium perborate. The adoption of the desensitizing varnish reduced the diffusion of peroxides significantly for all external bleaching materials. Peroxides penetrated the dentine during application of bleaching materials; the penetration of peroxides can be reduced by application of a desensitizing agent

Generalized Fiducial Inference on Differentiable Manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. This new approach uses generalized fiducial inference to obtain a posterior-like distribution on the manifold, without needing to know a parameterization that maps the constrained space to an unconstrained Euclidean space. The proposed methodology, called the constrained generalized fiducial distribution (CGFD), is obtained by using mathematical tools from Riemannian geometry. A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To demonstrate the practical use of the CGFD, we provide three proof-of-concept examples: inference for data from a multivariate normal density with the mean parameters on a sphere, a linear logspline density estimation problem, and a reimagined approach to the AR(1) model, all of which exhibit desirable coverages via simulation. We discuss two Markov chain Monte Carlo algorithms for the exploration of these constrained parameter spaces and adapt them for the CGFD.Comment: 31 pages, 7 figure

Defragmenting the Module Layout of a Partially Reconfigurable Device

Modern generations of field-programmable gate arrays (FPGAs) allow for partial reconfiguration. In an online context, where the sequence of modules to be loaded on the FPGA is unknown beforehand, repeated insertion and deletion of modules leads to progressive fragmentation of the available space, making defragmentation an important issue. We address this problem by propose an online and an offline component for the defragmentation of the available space. We consider defragmenting the module layout on a reconfigurable device. This corresponds to solving a two-dimensional strip packing problem. Problems of this type are NP-hard in the strong sense, and previous algorithmic results are rather limited. Based on a graph-theoretic characterization of feasible packings, we develop a method that can solve two-dimensional defragmentation instances of practical size to optimality. Our approach is validated for a set of benchmark instances.Comment: 10 pages, 11 figures, 1 table, Latex, to appear in "Engineering of Reconfigurable Systems and Algorithms" as a "Distinguished Paper

Phase-stabilized UV light at 267 nm through twofold second harmonic generation

Providing phase stable laser light is important to extend the interrogation time of optical clocks towards many seconds and thus achieve small statistical uncertainties. We report a laser system providing more than 50 µW phase-stabilized UV light at 267.4 nm for an aluminium ion optical clock. The light is generated by frequency-quadrupling a fibre laser at 1069.6 nm in two cascaded non-linear crystals, both in single-pass configuration. In the first stage, a 10 mm long PPLN waveguide crystal converts 1 W fundamental light to more than 0.2 W at 534.8 nm. In the following 50 mm long DKDP crystal, more than 50 µW of light at 267.4 nm are generated. An upper limit for the passive short-term phase stability has been measured by a beat-node measurement with an existing phase-stabilized quadrupling system employing the same source laser. The resulting fractional frequency instability of less than 5×10−17 after 1 s supports lifetime-limited probing of the 27Al+ clock transition, given a sufficiently stable laser source. A further improved stability of the fourth harmonic light is expected through interferometric path length stabilisation of the pump light by back-reflecting it through the entire setup and correcting for frequency deviations. The in-loop error signal indicates an electronically limited instability of 1 × 10−18 at 1 s

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics