Effect of Additives on Mineral Trioxide Aggregate Setting Reaction Product Formation

Introduction Mineral trioxide aggregate (MTA) sets via hydration of calcium silicates to yield calcium silicate hydrates and calcium hydroxide (Ca[OH]2). However, a drawback of MTA is its long setting time. Therefore, many additives have been suggested to reduce the setting time. The effect those additives have on setting reaction product formation has been ignored. The objective was to examine the effect additives have on MTA\u27s setting time and setting reaction using differential scanning calorimetry (DSC). Methods MTA powder was prepared with distilled water (control), phosphate buffered saline, 5% calcium chloride (CaCl2), 3% sodium hypochlorite (NaOCl), or lidocaine in a 3:1 mixture and placed in crucibles for DSC evaluation. The setting exothermic reactions were evaluated at 37°C for 8 hours to determine the setting time. Separate samples were stored and evaluated using dynamic DSC scans (37°C→640°C at10°C/min) at 1 day, 1 week, 1 month, and 3 months (n = 9/group/time). Dynamic DSC quantifies the reaction product formed from the amount of heat required to decompose it. Thermographic peaks were integrated to determine enthalpy, which was analyzed with analysis of variance/Tukey test (α = 0.05). Results Isothermal DSC identified 2 main exothermal peaks occurring at 44 ± 12 and 343 ± 57 minutes for the control. Only the CaCl2 additive was an accelerant, which was observed by a greater exothermic peak at 101 ± 11 minutes, indicating a decreased setting time. The dynamic DSC scans produced an endothermic peak around 450°C–550°C attributed to Ca(OH)2 decomposition. The use of a few additives (NaOCl and lidocaine) resulted in significantly less Ca(OH)2 product formation. Conclusions DSC was used to discriminate calcium hydroxide formation in MTA mixed with various additives and showed NaOCl and lidocaine are detrimental to MTA reaction product formation, whereas CaCl2 accelerated the reaction

Model Uncertainty based Active Learning on Tabular Data using Boosted Trees

Supervised machine learning relies on the availability of good labelled data for model training. Labelled data is acquired by human annotation, which is a cumbersome and costly process, often requiring subject matter experts. Active learning is a sub-field of machine learning which helps in obtaining the labelled data efficiently by selecting the most valuable data instances for model training and querying the labels only for those instances from the human annotator. Recently, a lot of research has been done in the field of active learning, especially for deep neural network based models. Although deep learning shines when dealing with image\textual\multimodal data, gradient boosting methods still tend to achieve much better results on tabular data. In this work, we explore active learning for tabular data using boosted trees. Uncertainty based sampling in active learning is the most commonly used querying strategy, wherein the labels of those instances are sequentially queried for which the current model prediction is maximally uncertain. Entropy is often the choice for measuring uncertainty. However, entropy is not exactly a measure of model uncertainty. Although there has been a lot of work in deep learning for measuring model uncertainty and employing it in active learning, it is yet to be explored for non-neural network models. To this end, we explore the effectiveness of boosted trees based model uncertainty methods in active learning. Leveraging this model uncertainty, we propose an uncertainty based sampling in active learning for regression tasks on tabular data. Additionally, we also propose a novel cost-effective active learning method for regression tasks along with an improved cost-effective active learning method for classification tasks

Constrained Monotonic Neural Networks

Wider adoption of neural networks in many critical domains such as finance and healthcare is being hindered by the need to explain their predictions and to impose additional constraints on them. Monotonicity constraint is one of the most requested properties in real-world scenarios and is the focus of this paper. One of the oldest ways to construct a monotonic fully connected neural network is to constrain signs on its weights. Unfortunately, this construction does not work with popular non-saturated activation functions as it can only approximate convex functions. We show this shortcoming can be fixed by constructing two additional activation functions from a typical unsaturated monotonic activation function and employing each of them on the part of neurons. Our experiments show this approach of building monotonic neural networks has better accuracy when compared to other state-of-the-art methods, while being the simplest one in the sense of having the least number of parameters, and not requiring any modifications to the learning procedure or post-learning steps. Finally, we prove it can approximate any continuous monotone function on a compact subset of $\mathbb{R}^n$

Distribution pattern of freshwater cyanobacteria in Kaiga region of Western Ghats of Karnataka

This study deals with the identification of 59 cyanobacterial species belonging to 27 genera from different freshwater habitats of Kaiga in Uttara Kannada district of Karnataka during the period from June 2009 to May 2011. Samplings were made during pre-monsoon; monsoon and post-monsoon sampling have been carried out for the duration of 2 years. The study deals with the occurrence of cyanobacterial species in five different aquatic habitats in Kaiga region, with respect to a change in the physico-chemical properties of water. The cyanobacterial diversity is maximum in monsoon season compared to post-monsoon; it was least during pre-monsoon. Among cyanobacteria non-heterocystous filamentous forms were dominant followed by unicellular forms, whereas heterocystous forms were least in number. It was also found that the physicho-chemical properties of water have the influence on the richness of cyanobacterial community. This study indicates the maximum occurrence and abundance of Chroococaceae (23.73%) and Phormidaceae (18.64%) members in all the sites, whereas Stigonemataceae (1.7%) shows very less occurrence. Among the cyanobacteria identified non-heterocystous filamentous forms were dominant followed by unicellular forms; whereas heterocystous forms were least in number.Â