Randomised study of the effects of fluoride and time on in situ remineralisation of acid-softened enamel

Objectives This single-centre, randomised, crossover study used a short-term in situ dental erosion remineralisation model to explore the remineralisation of acid-softened enamel in the 4-h period immediately following brushing with an anti-erosion, dentin hypersensitivity test dentifrice containing 1150 ppm fluoride (as sodium fluoride [NaF]) or a placebo dentifrice with no fluoride. Materials and methods Fifty participants wearing a palatal appliance holding surface-softened bovine enamel specimens brushed their natural teeth with their assigned dentifrice. Specimens were removed at 5-, 10-, 15-, 30-, 60-, 120- and 240-min post brushing. Enamel remineralisation effect was evaluated at each timepoint by percent surface microhardness recovery (%SMHR) and enamel fluoride uptake (EFU). After a second in vitro erosive challenge, the percent relative erosion resistance (%RER) was calculated. Results Statistically significant differences in %SMHR were observed for the test dentifrice compared with the placebo dentifrice from the 60-min timepoint onwards (all p < 0.02; mean difference of 8.66 [95% CI 3.46, 13.87] at 60 min). At each specimen removal time, %RER and EFU were statistically significantly higher for the test dentifrice compared with the placebo dentifrice (p < 0.0001 for all). No treatment-related or serious adverse events were reported. Conclusions The NaF-containing anti-erosion, dentin hypersensitivity dentifrice improved remineralisation of acid-softened enamel starting at 60 min of intra-oral exposure. It also improved enamel erosion resistance and fluoride uptake as early as 5 min after exposure to fluoridated dentifrice slurry

Surreal: Enhancing Surgical simulation Realism using style transfer

Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of cataract surgery using real data labels from an existing public dataset. Our results highlight the feasibility of the approach and also the powerful possibility to extend this technique to incorporate additional temporal constraints and to different applications

Declining Public Awareness of Heart Attack Warning Symptoms in the Years Following an Australian Public Awareness Campaign: A Cross-Sectional Study

Background: The National Heart Foundation of Australia's (NHFA) Warning Signs campaign ran between 2010 and 2013. This study examines trends in Australian adults’ ability to name heart attack symptoms during the campaign and in the years following. Methods: Using the NHFA's HeartWatch data (quarterly online surveys) for adults aged 30–59 years, we conducted an adjusted piecewise regression analysis comparing trends in the ability to name symptoms during the campaign period plus one year lag (2010–2014) to the post-campaign period (2015–2020) Results: Over the study period, there were 101,936 Australian adults surveyed. Symptom awareness was high or increased during the campaign period. However, there was a significant downward trend in each year following the campaign period for most symptoms (e.g., chest pain: adjusted odds ratio [AOR] =0.91, 95%CI: 0.56–0.80; arm pain: AOR=0.92, 95%CI: 0.90–0.94). Conversely, the inability to name any heart attack symptom increased in each year following the campaign (3.7% in 2010 to 19.9% in 2020; AOR=1.13, 95%CI: 1.10–1.15); these respondents were more likely to be younger, male, have less than 12 years of education, identify as Aboriginal and/or Torres Strait Islander Peoples, speak a language other than English at home and have no cardiovascular risk factors. Conclusion: Awareness of heart attack symptoms has decreased in the years since the Warning Signs campaign in Australia, with 1 in 5 adults currently unable to name a single heart attack symptom. New approaches are needed to promote and sustain this knowledge, and to ensure people act appropriately and promptly if symptoms occur

Reconstruction of MIS 5 climate in the central Levant using a stalagmite from Kanaan Cave, Lebanon

Lying at the transition between the temperate Mediterranean domain and subtropical deserts, the Levant is a key area to study the palaeoclimatic response over glacial–interglacial cycles. This paper presents a precisely dated last interglacial (MIS 5) stalagmite (129–84 ka) from the Kanaan Cave, Lebanon. Variations in growth rate and isotopic records indicate a warm humid phase at the onset of the last interglacial at ~ 129 ka that lasted until ~ 125 ka. A gradual shift in speleothem isotopic composition (125–122 ka) is driven mainly by the δ18O source effect of the eastern Mediterranean surface waters during sapropel 5 (S5). The onset of glacial inception began after ~ 122 ka, interrupted by a short wet pulse during the sapropel 4 (S4) event. Low growth rates and enriched oxygen and carbon values until ~ 84 ka indicate a transition to drier conditions during Northern Hemisphere glaciation

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems

CaDIS: Cataract dataset for surgical RGB-image segmentation

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/

Splitting Strong and Electromagnetic Interactions in K(L4) Decays

We recently considered $K_{\ell 4}$ decays in the framework of chiral perturbation theory based on the effective Lagrangian including mesons, photons, and leptons. There, we published analytic one-loop-level expressions for form factors $f$ and $g$ corresponding to the mixed process, $K^0\to\pi^0\pi^-\ell^+\nu_{\ell}$. We propose here a possible splitting between strong and electromagnetic parts allowing analytic (and numerical) evaluation of Isospin breaking corrections. The latter are sensitive to the infrared divergence subtraction scheme and are sizeable near the $\pi\pi$ production threshold. Our results should be used for the extraction of the $P$-wave iso-vector $\pi\pi$ phase shift from the outgoing data of the currently running KTeV experiment at FNAL.Comment: 47 pages, LaTeX, 6 postscript figure

A model for atomic and molecular interstellar gas: The Meudon PDR code

We present the revised ``Meudon'' model of Photon Dominated Region (PDR code), presently available on the web under the Gnu Public Licence at: http://aristote.obspm.fr/MIS. General organisation of the code is described down to a level that should allow most observers to use it as an interpretation tool with minimal help from our part. Two grids of models, one for low excitation diffuse clouds and one for dense highly illuminated clouds, are discussed, and some new results on PDR modelisation highlighted.Comment: accepted in ApJ sup

Predictive Model for Human-Unmanned Vehicle Systems

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles. However, the complex nature of these teams presents a difficult and exciting challenge for designers of human–unmanned vehicle systems. To build such systems effectively, models must be developed that describe the behavior of the human–unmanned vehicle team and that predict how alterations in team composition and system design will affect the system’s overall performance. In this paper, we present a method for modeling human–unmanned vehicle systems consisting of a single operator and multiple independent unmanned vehicles. Via a case study, we demonstrate that the resulting models provide an accurate description of observed human-unmanned vehicle systems. Additionally, we demonstrate that the models can be used to predict how changes in the human-unmanned vehicle interface and the unmanned vehicles’ autonomy alter the system’s performance.Lincoln Laborator