Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm.

Apical periodontitis (AP) is one of the most prevalent disorders in dentistry. However, it can be underdiagnosed in asymptomatic patients. In addition, the perioperative evaluation of 3-dimensional (3D) lesion volume is of great clinical relevance, but the required slice-by-slice manual delineation method is time- and labor-intensive. Here, for quickly and accurately detecting and segmenting periapical lesions (PALs) associated with AP on cone beam computed tomography (CBCT) images, we proposed and geographically validated a novel 3D deep convolutional neural network algorithm, named PAL-Net. On the internal 5-fold cross-validation set, our PAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.98. The algorithm also improved the diagnostic performance of dentists with varying levels of experience, as evidenced by their enhanced average AUC values (junior dentists: 0.89-0.94; senior dentists: 0.91-0.93), and significantly reduced the diagnostic time (junior dentists: 69.3 min faster; senior dentists: 32.4 min faster). Moreover, our PAL-Net achieved an average Dice similarity coefficient over 0.87 (0.85-0.88), which is superior or comparable to that of other existing state-of-the-art PAL segmentation algorithms. Furthermore, we validated the generalizability of the PAL-Net system using multiple external data sets from Central, East, and North China, showing that our PAL-Net has strong robustness. Our PAL-Net can help improve the diagnostic performance and speed of dentists working from CBCT images, provide clinically relevant volume information to dentists, and can potentially be applied in dental clinics, especially without expert-level dentists or radiologists.

Does pre-activating domain knowledge foster elaborated online information search strategies? Comparisons between young and old web user adults.

The present study aimed at investigating how pre-activating prior topic knowledge before browsing the web can support information search performance and strategies of young and older users. The experiment focus on analyzing to what extent prior knowledge pre-activation might cope with older users' difficulties when interacting with a search engine. 26 older (age 60 to 77) and 22 young (age 18 to 32) adults performed 6 information search problems related to health and fantastic movies. Overall, results showed that pre-activating prior topic knowledge increased the time spent evaluating the search engine results pages, fostered deeper processing of the navigational paths elaborated (and thus reduced the exploration of different navigational paths) and improved the semantic specificity of queries. Pre-activating prior knowledge helped older adults produced semantically more specific queries when they had lower prior-knowledge than young adults. Moderation analyses indicated that the pre-activation supported older adults' search performance under the condition that participants generated semantically relevant keywords during this pre-activation task. Implications of these results show that prior topic knowledge pre-activation may be a good lead to support the beneficial role of prior knowledge in older users' search behavior and performance. Recommendations for design pre-activation support tool are provided.

Iridium-based double perovskites for efficient water oxidation in acid media.

The development of active, cost-effective and stable oxygen-evolving catalysts is one of the major challenges for solar-to-fuel conversion towards sustainable energy generation. Iridium oxide exhibits the best available compromise between catalytic activity and stability in acid media, but it is prohibitively expensive for large-scale applications. Therefore, preparing oxygen-evolving catalysts with lower amounts of the scarce but active and stable iridium is an attractive avenue to overcome this economical constraint. Here we report on a class of oxygen-evolving catalysts based on iridium double perovskites which contain 32 wt% less iridium than IrO2 and yet exhibit a more than threefold higher activity in acid media. According to recently suggested benchmarking criteria, the iridium double perovskites are the most active catalysts for oxygen evolution in acid media reported until now, to the best of our knowledge, and exhibit similar stability to IrO2.

Conductance switching in Ag(2)S devices fabricated by in situ sulfurization.

We report a simple and reproducible method to fabricate switchable Ag(2)S devices. The alpha-Ag(2)S thin films are produced by a sulfurization process after silver deposition on an Si substrate. Structure and composition of the Ag(2)S are characterized using XRD and RBS. Our samples show semiconductor behaviour at low bias voltages, whereas they exhibit reproducible bipolar resistance switching at higher bias voltages. The transition between both types of behaviour is observed by hysteresis in the I-V curves, indicating decomposition of the Ag(2)S, increasing the Ag(+) ion mobility. The as-fabricated Ag(2)S samples are a good candidate for future solid state memory devices, as they show reproducible memory resistive properties and they are fabricated by an accessible and reliable method.

ACT-PRO action protocol analyzer: a tool for analyzing discrete action protocols.

This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, called discrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, > 10(5)) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter's attention on data that do not fit the prespecified patterns.