655 research outputs found
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 10ⶠ± 5.4 x 10ⶠbacteria/cmÂČ) than on enamel (1.2 x 10ⶠ± 1.5 x 10ⶠbacteria/cmÂČ , DAPI). Chlorhexidine significantly reduced the amount of adherent bacteria (dentin: 2.8 x 10┠± 3.4 x 10â” bacteria/cmÂČ ; enamel: 4.2 x 10┠± 8.7 x 10â” bacteria/cmÂČ). Rinses with the fluoride solution also significantly reduced bacterial adherence to dentin (8.1 x 10┠± 1.5 x 10ⶠbacteria/cmÂČ). 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 (VivasensÂź) 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; nâ=â8 samples per subgroup). The penetration of peroxides was measured photometrically using 4-aminoantipyrin as a substrate, the penetration of peroxides was monitored over 240Â 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 ”W 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 ”W 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 5Ă10â17 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 Ă 10â18 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
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