Knowledge, awareness, and perception on root canal treatment among South Indian population - A survey

The pulp is removed, and the interior of the tooth is cleaned and sealed during root canal treatment. Root canals are dreaded by many people because they are considered to be painful. The following are indicators of pulpal damage, color changes, swelling, thermal sensitivity, and pain in the gums. The pulp gets inflamed or diseased when it becomes infected, then RCT or extraction is needed to eliminate the inflamed or infected tissue and heal that particular region. The questionnaire was circulated to the sample population through a Google Docs link. Pie charts with a frequency table were created when the survey data were acquired. The Chi-square test was used to evaluate the connection using the SPSS software. Eighty-four percent of people are aware of RCT and what it is intended for and 80% of the participants themselves have undergone endodontic treatment. Most of the South Indian population were aware of root canal treatment and have undergone an RCT procedure.

RepeatsPlus - program for finding motifs and repeats in data sequences

Discovering motifs and repeats in data sequences is of great importance in biology and a large number of efficient tools for their finding have been developed. As the number of results found can be very large, our goal is to provide a tool that, on a mathematical basis, can precisely find all motifs and repeats, filter them according to input arguments and output the results in a convenient way. RepeatsPlus is a program that provides statistical filtering according to input sequence length and number of repeat occurrences, motif mask filtering and filtering related to ambiguous letters in input sequence and a large number of other options. RepeatPlus is implemented in Python and C[Formula: see text]. It is freely available for public use. The user manual and examples of usage are also available.

Finding Statistically Significant Repeats in Nucleic Acids and Proteins

DNA repeats have great importance for biological research and a large number of tools for determining repeats have been developed. Herein we define a method for extracting a statistically significant subset of a determined set of repeats. Our aim was to identify a subset of repeats in the input sequences that are not expected to occur with a number of their appearances in a random sequence of the same length. It is expected that results obtained in such manner would reduce the quantity of processed material and could thereby represent a more important biological signal. With DNA, RNA, and protein sequences serving as input material, we also examined the possibility of statistical filtering of repeats in sequences over an arbitrary alphabet. A new method for selecting statistically significant repeats from a set of determined repeats has been defined. The proposed method was tested on a large number of randomly generated sequences. The application of the method on biological sequences revealed that for some viruses, shorter repeats are more statistically significant than longer ones because of their frequent appearance, whereas for bacteria, the majority of identified repeats are statistically significant.

Sample size estimation in clinical trial

Every clinical trial should be planned. This plan should include the objective of trial, primary and secondary end-point, method of collecting data, sample to be included, sample size with scientific justification, method of handling data, statistical methods and assumptions. This plan is termed as clinical trial protocol. One of the key aspects of this protocol is sample size estimation. The aim of this article is to discuss how important sample size estimation is for a clinical trial, and also to understand the effects of sample size over- estimation or under-estimation on outcome of a trial. Also an attempt is made to understand importance of minimum sample to detect a clinically important difference. This article is also an attempt to provide inputs on different parameters that impact sample size and basic rules for these parameters with the help of some simple examples.