Influence of occlusal access on demineralized dentin removal in the Atraumatic Restorative Treatment (ART) approach

Purpose: To verify the influence of cavity access diameter on demineralized dentin removal in the ART approach. Methods: 40 non-carious human premolars were randomly divided into four groups. The occlusal surface was ground flat and the teeth were sectioned mesio-distally. The hemi-sections were reassembled and occlusal access preparations were carried out using ball-shaped diamonds. The resulting size of the occlusal opening was 1.0 mm, 1.4 mm, 1.6 mm and 1.8 mm for Groups A, B, C, and D, respectively. Standardized artificial carious lesions were created and demineralized dentin was excavated. After excavation, the cavities were analyzed using: (a) the tactile method, (b) caries-detection dye to stain demineralized dentin, as proposed by Smales & Fang, and (c) Demineralized Tissue Removal index, as proposed in this study. Statistical analysis was performed using Fisher, Spearman correlation coefficient, kappa, Kruskal-Wallis and Miller tests (P < 0.05). Results: The three methods of evaluation showed no significant difference between Groups A vs. B, and C vs. D, while statistically significant differences were observed between Groups A vs. C, A vs. D, B vs. C and B vs. D. Based on the results of this study, the size of occlusal access significantly affected the efficacy of demineralized tissue removal.

Ten-year survival of ART restorations in permanent posterior teeth

This study evaluated the 10-year clinical performance of high-viscosity glass-ionomer cement placed in posterior permanent teeth by means of the Atraumatic Restorative Treatment (ART) approach. One operator placed 167 single- and 107 multiple-surface restorations in 43 high-risk caries pregnant women (mean decayed teeth = 9.8 +/- 5.5). Examinations were performed at 1-, 2-, and 10-year intervals according to ART criteria. In the last evaluation, the US Public Health Service (USPHS) criteria were also used. After 10 years, 129 restorations (47.1%) were evaluated and achieved a cumulative survival rate of 49.0% (SE 7.2%). The 10-year survival of single- and multiple-surface ART restorations assessed using the ART criteria were 65.2% (SE 7.3%) and 30.6% (SE 9.9%), respectively. This difference was significant (jackknife SE of difference; p < 0.05). Using the USPHS criteria, the 10-year survival of single- and multiple-surface ART restorations were 86.5% and 57.6%, respectively. The primary causes of failure were total loss (9.3%) and marginal defects (5.4%). The survival rates observed, especially for the single-surface restorations, confirm the potential of the ART approach for restoring and saving posterior permanent teeth.

Six-year evaluation of the atraumatic restorative treatment approach in permanent-tooth Class III restorations

Purpose: The aim of this study was to assess the 6-year performance of the ART (atraumatic restorative treatment) approach in Class III restorations in permanent teeth. Materials and Methods: A total of 127 ART Class III restorations, using Ketac-Molar (3M ESPE) ionomer cement, was performed in 58 adult patients by one experienced operator in 1998. After a 6 years, 34 patients and 65 restorations were evaluated according to ART criteria. Two calibrated examiners carried out the evaluation. Data were analyzed by exact 95% Confidence Interval and Survival Analysis using the Jackknife method for standard error determination. Results: Among assessed restorations, 73.8% (95% CI = 61.5% to 86.2%) were in good condition and classified as successful, with a 67.6% (95% CI = 54.4% to 80.7%) cumulative survival rate. Failed restorations included 13.9% completely or partially missing restorations, 9.2% restorations that had been replaced by other treatment, 1.5% restorations with a large defect at the margin, and 1.5% restorations that presented high wear on the surface. No caries was observed even in those teeth in which restorations were absent. Conclusion: The 6-year success rate of the ART approach in anterior permanent teeth (Class III) was considered high.

Clinical evaluation of multisurface ART restorations

O objetivo deste estudo foi avaliar o comportamento de dois cimentos de ionômero de vidro: um de alta viscosidade (Ketac Molar - ESPE) e um modificado por resina (Fuji VIII – GC) em restaurações envolvendo duas ou mais superfícies dentárias, confeccionadas pela técnica do Tratamento Restaurador Atraumático. Sessenta restaurações (30 com cada material) foram inseridas em estudantes (9-16 anos) por dois operadores. Após 6 meses, dois examinadores independentes avaliaram as restaurações de acordo com os critérios utilizados em trabalhos prévios semelhantes. Os dados foram analisados pelos testes de McNemar e Fischer. A porcentagem de sucesso do tratamento foi de 98,3%. Uma restauração (Ketac Molar) foi substituída por outro material e classificada como falha. As porcentagens de sucesso das restaurações foram de 100% e 96,6% para o Fuji VIII e Ketac Molar, respectivamente. Não houve diferença estatisticamente significante no sucesso das restaurações entre o baseline e 6 meses (p>0,05). Da mesma forma, não houve diferença estatística entre os materiais, tipos de cavidade ou entre operadores.(p>0,05). A técnica ART foi altamente apropriada e efetiva em restaurações envolvendo duas ou mais superfícies, após 6 meses. Os resultados mostraram um comportamento promissor com ambos os materiais.

Reshaping Rural Extension: Learning for Sustainability (LforS): An Integrative and Learning-Based Advisory Approach for Rural Extension with Small-Scale Farmers

This publication offers concrete suggestions for implementing an integrative and learning-oriented approach to agricultural extension with the goal of fostering sustainable development. It targets governmental and non-governmental organisations, development agencies, and extension staff working in the field of rural development. The book looks into the conditions and trends that influence extension today, and outlines new challenges and necessary adaptations. It offers a basic reflection on the goals, the criteria for success and the form of a state-of-the-art approach to extension. The core of the book consists of a presentation of Learning for Sustainability (LforS), an example of an integrative, learning-oriented approach that is based on three crucial elements: stakeholder dialogue, knowledge management, and organizational development. Awareness raising and capacity building, social mobilization, and monitoring & evaluation are additional building blocks. The structure and organisation of the LforS approach as well as a selection of appropriate methods and tools are presented. The authors also address key aspects of developing and managing a learning-oriented extension approach. The book illustrates how LforS can be implemented by presenting two case studies, one from Madagascar and one from Mongolia. It addresses conceptual questions and at the same time it is practice-oriented. In contrast to other extension approaches, LforS does not limit its focus to production-related aspects and the development of value chains: it also addresses livelihood issues in a broad sense. With its focus on learning processes LforS seeks to create a better understanding of the links between different spheres and different levels of decision-making; it also seeks to foster integration of the different actors’ perspectives.

The atraumatic restorative treatment approach: an "atraumatic" alternative

INTRODUCTION Fear and anxiety are part of all human experiences and they may contribute directly to a patient's behavior. The Atraumatic Restorative Treatment (ART) is a technique that may be an alternative approach in treating special care patients or those who suffer fear or anxiety. OBJECTIVE the aim of this paper is to review the ART technique as an alternative to reduce pain and fear during dental treatment. MATERIAL AND METHODS A search for the term "atraumatic restorative treatment" was carried out in the MEDLINE search engine. References, from the last 10 years, containing at least one of the terms: "psychological aspects", "discomfort", "fear", "anxiety" or "pain", were selected. RESULTS A total of 120 references were found, from which only 17 fit the criteria. Discussion: All authors agreed that the ART promotes less discomfort for patients, contributing to a reduction of anxiety and fear during the dental treatment. Results also indicated that ART minimizes pain reported by patients. CONCLUSIONS The ART approach can be considered as having favorable characteristics for the patient, promoting an "atraumatic" treatment. This technique may be indicated for patients who suffer from fear or anxiety towards dental treatments and whose behavior may cause the treatment to become unfeasible or even impossible altogether.

Traduction statistique par recherche locale

La traduction statistique vise l’automatisation de la traduction par le biais de modèles statistiques. Dans ce travail, nous relevons un des grands défis du domaine : la recherche (Brown et al., 1993). Les systèmes de traduction statistique de référence, tel Moses (Koehn et al., 2007), effectuent généralement la recherche en explorant l’espace des préfixes par programmation dynamique, une solution coûteuse sur le plan computationnel pour ce problème potentiellement NP-complet (Knight, 1999). Nous postulons qu’une approche par recherche locale (Langlais et al., 2007) peut mener à des solutions tout aussi intéressantes en un temps et un espace mémoire beaucoup moins importants (Russell et Norvig, 2010). De plus, ce type de recherche facilite l’incorporation de modèles globaux qui nécessitent des traductions complètes et permet d’effectuer des modifications sur ces dernières de manière non-continue, deux tâches ardues lors de l’exploration de l’espace des préfixes. Nos expériences nous révèlent que la recherche locale en traduction statistique est une approche viable, s’inscrivant dans l’état de l’art.

Antibacterial activity of four glass ionomer cements used in atraumatic restorative treatment

The in vitro antibacterial activity of four glass ionomer cements ( Fuji IX, Ketac Molar, Vidrion R and Vitromolar) indicated for Atraumatic Restorative Treatment ( ART) was studied against strains of bacteria involved in the development of oral diseases, Streptococcus mutans, Streptococcus sobrinus, Lactobacillus acidophilus and Actinomyces viscosus. The agar plate diffusion test was used for the cultures, which included chlorhexidine as a positive control. The results demonstrated that all the cements evaluated presented antibacterial activity. Based on the results of this study, it can be concluded that Fuji IX and Ketac Molar presented the most effective antibacterial activity considering the ART approach.

Synchronization of two independently moving cameras without feature correspondences

In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and/or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach.

Using min-sum loopy belief propagation for decentralised supply chain formation

Modern business trends such as agile manufacturing and virtual corporations require high levels of flexibility and responsiveness to consumer demand, and require the ability to quickly and efficiently select trading partners. Automated computational techniques for supply chain formation have the potential to provide significant advantages in terms of speed and efficiency over the traditional manual approach to partner selection. Automated supply chain formation is the process of determining the participants within a supply chain and the terms of the exchanges made between these participants. In this thesis we present an automated technique for supply chain formation based upon the min-sum loopy belief propagation algorithm (LBP). LBP is a decentralised and distributed message-passing algorithm which allows participants to share their beliefs about the optimal structure of the supply chain based upon their costs, capabilities and requirements. We propose a novel framework for the application of LBP to the existing state-of-the-art case of the decentralised supply chain formation problem, and extend this framework to allow for application to further novel and established problem cases. Specifically, the contributions made by this thesis are: • A novel framework to allow for the application of LBP to the decentralised supply chain formation scenario investigated using the current state-of-the-art approach. Our experimental analysis indicates that LBP is able to match or outperform this approach for the vast majority of problem instances tested. • A new solution goal for supply chain formation in which economically motivated producers aim to maximise their profits by intelligently altering their profit margins. We propose a rational pricing strategy that allows producers to earn significantly greater profits than a comparable LBP-based profitmaking approach. • An LBP-based framework which allows the algorithm to be used to solve supply chain formation problems in which goods are exchanged in multiple units, a first for a fully decentralised technique. As well as multiple-unit exchanges, we also model in this scenario realistic constraints such as factory capacities and input-to-output ratios. LBP continues to be able to match or outperform an extended version of the existing state-of-the-art approach in this scenario. • Introduction of a dynamic supply chain formation scenario in which participants are able to alter their properties or to enter or leave the process at any time. Our results suggest that LBP is able to deal easily with individual occurences of these alterations and that performance degrades gracefully when they occur in larger numbers.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

Effectively positioning water loss event in smart water networks

With the eye-catching advances in sensing technologies, smart water networks have been attracting immense research interest in recent years. One of the most overarching tasks in smart water network management is the reduction of water loss (such as leaks and bursts in a pipe network). In this paper, we propose an efficient scheme to position water loss event based on water network topology. The state-of-the-art approach to this problem, however, utilizes the limited topology information of the water network, that is, only one single shortest path between two sensor locations. Consequently, the accuracy of positioning water loss events is still less desirable. To resolve this problem, our scheme consists of two key ingredients: First, we design a novel graph topology-based measure, which can recursively quantify the "average distances" for all pairs of senor locations simultaneously in a water network. This measure will substantially improve the accuracy of our positioning strategy, by capturing the entire water network topology information between every two sensor locations, yet without any sacrifice of computational efficiency. Then, we devise an efficient search algorithm that combines the "average distances" with the difference in the arrival times of the pressure variations detected at sensor locations. The viable experimental evaluations on real-world test bed (WaterWiSe@SG) demonstrate that our proposed positioning scheme can identify water loss event more accurately than the best-known competitor.

Neural Radiance Fields for Relighting and View Synthesis

Neural scene representation and neural rendering are new computer vision techniques that enable the reconstruction and implicit representation of real 3D scenes from a set of 2D captured images, by fitting a deep neural network. The trained network can then be used to render novel views of the scene. A recent work in this field, Neural Radiance Fields (NeRF), presented a state-of-the-art approach, which uses a simple Multilayer Perceptron (MLP) to generate photo-realistic RGB images of a scene from arbitrary viewpoints. However, NeRF does not model any light interaction with the fitted scene; therefore, despite producing compelling results for the view synthesis task, it does not provide a solution for relighting. In this work, we propose a new architecture to enable relighting capabilities in NeRF-based representations and we introduce a new real-world dataset to train and evaluate such a model. Our method demonstrates the ability to perform realistic rendering of novel views under arbitrary lighting conditions.

Voice conversion with pre-trained representations for audio anonymization

The recording and processing of voice data raises increasing privacy concerns for users and service providers. One way to address these issues is to move processing on the edge device closer to the recording so that potentially identifiable information is not transmitted over the internet. However, this is often not possible due to hardware limitations. An interesting alternative is the development of voice anonymization techniques that remove individual speakers characteristics while preserving linguistic and acoustic information in the data. In this work, a state-of-the-art approach to sequence-to-sequence speech conversion, ini- tially based on x-vectors and bottleneck features for automatic speech recognition, is explored to disentangle the two acoustic information using different pre-trained speech and speakers representation. Furthermore, different strategies for selecting target speech representations are analyzed. Results on public datasets in terms of equal error rate and word error rate show that good privacy is achieved with limited impact on converted speech quality relative to the original method.