Dental approach to erosive tooth wear in gastroesophageal reflux disease

Background: The duration of gastro-esophageal reflux disease (GERD), the frequency of reflux, the pH and type of acid, and the quality and quantity of saliva affect the severity of dental erosion due to GERD.Objective: To summarize the diagnostic protocol and treatment of dental erosion due to GERD.Methods: A Medline literature search was performed to identify articles associated with a dental approach to GERD.Results: The dental professional must carry out a diagnostic protocol, which includes collecting data on the patient’s medical and dietary history, occupational/recreational history, dental history, and oral hygiene methods. Intraoral, head and neck, and salivary function examinations should be performed to expose the dental implications of GERD symptoms.Conclusion: Diagnosing the cause of erosive tooth wear can help prevent further damage. Patients must be informed about how to prevent GERD.Keywords: Dental erosion, gastro-esophageal reflux diseas

Electromechanical wavelength tuning of double-membrane photonic crystal cavities

We present a method for tuning the resonant wavelength of photonic crystal cavities (PCCs) around 1.55 um. Large tuning of the PCC mode is enabled by electromechanically controlling the separation between two parallel InGaAsP membranes. A fabrication method to avoid sticking between the membranes is discussed. Reversible red/blue shifting of the symmetric/anti-symmetric modes has been observed, which provides clear evidence of the electromechanical tuning, and a maximum shift of 10 nm with < 6 V applied bias has been obtained.Comment: 9 pages, 3 figure

Yield performances and nutritional contents of three oyster mushroom species cultivated on wheat stalk

This study was conducted to determine nutritive value and yield performance of the three types of oyster mushroom; Pleurotus eryngii (Dc. Ex Fr.) quel), Pleurotus ostreatus (Jacq.: Fr.) Kumm.) andPleurotus sajor-caju (Fr.) Singer, cultivated on wheat stalk. The total fresh mushroom yields obtained with 100 g material (70% misture) after the three harvests and the total harvest time were calculated. P.sajor-caju gave the highest yield as 20.2 g. The yield of P. ostreatus was 17.9 g and the lowest yield was P. eryngii, 4.5 g. Total harvest time of mushrooms were determined. As the P. sajor-caju was harvestedin 67.46 days, P. ostreatus was harvested in 82.64 days and P. eryngii was harvested in 85.27 days. For chemical composition analysis the fruiting bodies of mushrooms were collected after the first productive flow and dried in an oven at 60°C at a constant weight and kept under refrigeration at 4°C. Energy, protein, fat, carbohydrate, dietary fibre, moisture, ash (g in 100 g dried matter) and amino acids(mg in 1 g dried matter) of mushrooms were analysed. In P. eryngii and P. sajor-caju the highest amount of amino acid was from aspartic acid and the lowest was from methionine. The highest and the lowestamino acid amount in P. ostreatus were from glutamic acid and methionine, respectively. The histidine amino acid was just detected in P. eryngii but hydroxy-L-proline was not detected in mushrooms. Theenergy (kcal/100 g dried matter), fat, protein, carbohydrate, dietary fibre, moisture and ash (g/100 g dried matter) values of P. eryngii were 276.33, 11.95, 7.50, 39.85, 28.45, 7.23 and 4.89, respevtively.These values for P. ostreatus were 243.66, 17.12, 2.60, 37.87, 30.25, 7.39 and 4.78, respectively. The values for P. sajor-caju were 229.22, 16.75, 1.15, 37.72, 30.67, 7.42 and 5.84, respectively

Effect of using different lignocellulosic wastes for cultivation of Pleurotus ostreatus (Jacq.) P. Kumm. on mushroom yield, chemical composition and nutritional value

In this study, the mushroom yield, chemical composition and nutritional value of Pleurotus ostreatus (Jacq.) P. Kumm. cultivated in wheat stalk (WS), millet stalk (MS), soybean stalk (SS) and cotton stalk (CS) were determined. Fresh mushroom yield amounts (100 g of substrate, 70% moisture) obtained from WS, CS, MS and SS substrate media were 17.9, 14.3, 22.7 and 31.5 g, respectively. Samples of mushroom cultivated on different culture mediums were analysed for protein, energy, ash, fat, dietary fibre, carbohydrate, moisture, vitamins (thiamin, riboflavin, pyridoxin and niacin) and amino acid contents

The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

О параметрах системы подготовки принятия решений государственной организации с помощью бизнес-процессов

В статье приводится описание параметров, необходимых для информационной системы принятия решений государственной организации при оказании услуг с помощью бизнес-процессов. Любая информационная система, позволяющая подготавливать данные для принятия решений, строится на основе количественной информации. Однако, само решение выбирается чаще всего на основе опыта, знаний, что субъективно и не во всех случаях является правильным. Нами предлагается выделить класс событий, для которых возможно разработать шаблоны решений. Выбор решения основывается на анализе параметров бизнес-процессов государственной организации при оказании услуг.The article describes the parameters necessary for the decision-making information system of the state organization in the provision of services through business processes. Any information system that allows data to be prepared for decision-making is based on quantitative information. However, the decision itself is chosen most often on the basis of experience, knowledge, which is subjective and not always correct. We propose to allocate a class of events for which it is possible to develop decision templates. The choice of the solution is based on the analysis of the parameters of the business processes of the state organization in the provision of service

Automated assessment of disease progression in Acute Myeloid Leukemia by probabilistic analysis of flow cytometry data

Objective: Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a post-therapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced non-parametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data. Methods: The highly flexible non-parametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are in turn used to predict the direction of change in disease progression for each patient. Results: We used 200 diseased and non-diseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (9 out of 10) for relapsing cases and 100% (26 out of 26) for the remaining cases. Conclusions: We believe that these promising results are an important first step towards the development of automated predictive systems for disease monitoring and continuous response evaluation. Significance: Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies