title: Development of an innovative urease-aided self-healing dental composite

authors: Seifan, Mostafa

subjects: self-healing

abstract: Dental restorative materials suffer from major drawbacks, namely fracture and shrinkage, which result in failure and require restoration and replacement. There are different methods to address these issues, such as increasing the filler load or changing the resin matrix of the composite. In the present work, we introduce a new viable process to heal the generated cracks with the aid of urease enzyme. In this system, urease breaks down the salivary urea which later binds with calcium to form calcium carbonate (CaCO₃). The formation of insoluble CaCO₃ fills any resultant fracture or shrinkage from the dental composure hardening step. The healing process and the formation of CaCO₃ within dental composites were successfully confirmed by optical microscope, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDS) methods. This research demonstrates a new protocol to increase the service life of dental restoration composites in the near future.

text: Development of an innovative urease-aided self-healing dental composite Dental restorative materials suffer from major drawbacks, namely fracture and shrinkage, which result in failure and require restoration and replacement. There are different methods to address these issues, such as increasing the filler load or changing the resin matrix of the composite. In the present work, we introduce a new viable process to heal the generated cracks with the aid of urease enzyme. In this system, urease breaks down the salivary urea which later binds with calcium to form calcium carbonate (CaCO₃). The formation of insoluble CaCO₃ fills any resultant fracture or shrinkage from the dental composure hardening step. The healing process and the formation of CaCO₃ within dental composites were successfully confirmed by optical microscope, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDS) methods. This research demonstrates a new protocol to increase the service life of dental restoration composites in the near future.

year: 2020

title: Predictions and simulations of cortical dynamics during natural sleep using a continuum approach

authors: Wilson, Marcus T.

subjects: REM

abstract: In this paper we use a continuum model in two spatial dimensions to study the dynamics of the cortex during natural sleep, including explicitly the effects of two key neuromodulators. The model predicts that a number of states could be available to the cortex. We identify two of these with slow-wave sleep and rapid eye movement (REM) sleep, and focus on the transition between the two. Eigenvalue analysis of the linearized model, together with simulations on a two-dimensional grid, show that a number of oscillatory states exist; the occurrence of these is particularly dependent upon the duration in time of the inhibitory postsynaptic potential. These oscillatory states are similar to the cortical slow oscillation and certain types of seizure. Power spectra are evaluated for different parameter sets and compare favorably with experiment. Grid simulations show that transitions between cortical states (e.g., slow-wave to REM) can be seeded at any point in space by random fluctuations in subcortical input.

text: Predictions and simulations of cortical dynamics during natural sleep using a continuum approach In this paper we use a continuum model in two spatial dimensions to study the dynamics of the cortex during natural sleep, including explicitly the effects of two key neuromodulators. The model predicts that a number of states could be available to the cortex. We identify two of these with slow-wave sleep and rapid eye movement (REM) sleep, and focus on the transition between the two. Eigenvalue analysis of the linearized model, together with simulations on a two-dimensional grid, show that a number of oscillatory states exist; the occurrence of these is particularly dependent upon the duration in time of the inhibitory postsynaptic potential. These oscillatory states are similar to the cortical slow oscillation and certain types of seizure. Power spectra are evaluated for different parameter sets and compare favorably with experiment. Grid simulations show that transitions between cortical states (e.g., slow-wave to REM) can be seeded at any point in space by random fluctuations in subcortical input.

year: 2005

title: Harnessing Indigenous Tweets: The Reo Maori Twitter corpus

authors: Trye, David

subjects: Science & Technology

abstract: Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike.

text: Harnessing Indigenous Tweets: The Reo Maori Twitter corpus Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike.

year: 2022-02-14