scholarly journals Microbial population shift and metabolic characterization of silver diamine fluoride treatment failure on dental caries

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0242396
Author(s):  
Bidisha Paul ◽  
Maria A. Sierra ◽  
Fangxi Xu ◽  
Yasmi O. Crystal ◽  
Xin Li ◽  
...  
Keyword(s):  
Dental Caries ◽  
Small Sample Size ◽  
Small Sample ◽  
Sample Collection ◽  
Phosphotransferase System ◽  
Differential Abundance ◽  
Silver Diamine Fluoride ◽  
Linear Discriminant ◽  
Fluoride Treatment ◽  
Carious Lesions

The objective of this pilot study was to describe the microbial profiles present in the plaque and saliva of children who continued to develop new carious lesions following treatment with silver diamine fluoride (“nonresponders”) compared to caries active, caries-free, and children immediately receiving SDF treatment for untreated caries in order to identify potential microbial differences that may relate to a re-incidence of caries. Saliva and plaque samples from infected and contralateral sites were obtained from twenty children who were either caries free, had active carious lesions, were caries active and received SDF treatment immediately before sampling, or had previously received SDF treatment and developed new caries. In total, 8,057,899 Illumina-generated sequence reads from 60 samples were obtained. Reads were processed using the Quantitative Insights Into Microbial Ecology pipeline. Group differences were assessed using Analysis of Variance Models and Tukey Honest Significant Differences. To identify significant taxa between treatment groups, Linear discriminant analysis Effect Size (LefSe) and Analysis of Differential Abundance Taking Sample Variation Into Account were used. Differential abundant analysis indicated that members of the Lachnospiraceae family were significantly enriched in non-responders and the genus Tannerella and species Granulicatella adiances were also highly abundant in this group. LefSe analysis between non-responders and SDF-treated groups revealed that genera Leptotrichia and Granulicatella were enriched in non-responders. We observed the highest abundance of phosphotransferase system and lowest abundance of lipopolysaccharide synthesis in non-responders. The microbiome in dental biofilms is responsible for initiation and progression of dental caries. SDF has been shown to be effective in arresting the progression carious lesions, in part due to its antimicrobial properties. Findings suggest that the differential abundance of select microbiota and specific pathway functioning in individuals that present with recurrent decay after SDF treatment may contribute to a potential failure of silver diamine fluoride to arrest dental caries. However, the short duration of sample collection following SDF application and the small sample size emphasize the need for further data and additional analysis.

scholarly journals Microbial Population Shift and Metabolic Characterization of Silver Diamine Fluoride Treatment Failure on Dental Caries

2020 ◽  
Author(s):  
Bidisha Paul ◽  
Maria A Sierra ◽  
Fangxi Xu ◽  
Yasmi O Crystal ◽  
Xin Li ◽  
...  
Keyword(s):  
Dental Caries ◽  
Antimicrobial Properties ◽  
Phosphotransferase System ◽  
Differential Abundance ◽  
Silver Diamine Fluoride ◽  
Linear Discriminant ◽  
Fluoride Treatment ◽  
Treatment Groups ◽  
Carious Lesions ◽  
Potential Failure

AbstractObjectivesThe objective of this pilot study was to describe the microbial profiles present in the plaque and saliva of children who continued to develop new carious lesions following treatment with silver diamine fluoride (“nonresponders”) compared to caries active, cariesfree, and children immediately receiving SDF treatment for untreated carie s in order to identify potential microbial differences that may relate to a re-incidence of caries.MethodsSaliva and plaque samples from infected and contralateral sites were obtained from twenty children who were either caries free, had active carious lesions, were caries active and received SDF treatment immediately before sampling, or had previously received SDF treatment and developed new caries. In total, 8,057,899 Illumina-generated sequence reads from 60 samples were obtained. Reads were processed using the Quantitative Insights Into Microbial Ecology pipeline. Group differences were assessed using Analysis of Variance Models and Tukey Honest Significant Differences. To identify significant taxa between treatment groups, Linear discriminant analysis Effect Size (LefSe) and Analysis of Differential Abundance Taking Sample Variation Into Account were used.ResultsDifferential abundant analysis indicated that members of the Lachnospiraceae family were significantly enriched in non-responders and the genus Tannerella and species Granulicatella adiances were also highly abundant in this group. LefSe analysis between nonresponders and SDF-treated groups revealed that genera Leptotrichia and Granulicatella were enriched in non-responders. We observed the highest abundance of phosphotransferase system and lowest abundance of lipopolysaccharide synthesis in non-responders.DiscussionThe microbiome in dental biofilms is responsible for initiation and progression of dental caries. SDF has been shown to be effective in arresting the progression carious lesions, in part due to to its antimicrobial properties. Findings suggest that the differential abundance of select microbiota and specific pathway functioning in individuals that present with recurrent decay after SDF treatment may contribute to a potential failure of silver diamine fluoride to arrest dental caries.

A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images

2012 ◽  
pp. 18-33 ◽  
Author(s):  
Carlos Eduardo Thomaz ◽  
Vagner do Amaral ◽  
Gilson Antonio Giraldi ◽  
Edson Caoru Kitani ◽  
João Ricardo Sato ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Face Image ◽  
Small Sample ◽  
High Dimensional ◽  
Data Set ◽  
Linear Discriminant ◽  
Face Images ◽  
Linear Framework ◽  
Facial Changes ◽  
2D Data

This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The approach is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D data set of frontal face images, the authors determine a most characteristic direction of change by organizing the data according to the patterns of interest. These experiments on publicly available face image sets show that the multi-linear approach does produce visually plausible results for gender, facial expression and aging facial changes in a simple and efficient way. The authors believe that such approach could be widely applied for modeling and reconstruction in face recognition and possibly in identifying subjects after a lapse of time.

Discriminant Analysis for Biometric Recognition

2011 ◽  
pp. 25-29
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Sample Size ◽  
Linear Discriminant Analysis ◽  
Small Sample Size ◽  
Small Sample ◽  
Biometric Recognition ◽  
Linear Discriminant

This chapter is a brief introduction to biometric discriminant analysis technologies — Section I of the book. Section 2.1 describes two kinds of linear discriminant analysis (LDA) approaches: classification-oriented LDA and feature extraction-oriented LDA. Section 2.2 discusses LDA for solving the small sample size (SSS) pattern recognition problems. Section 2.3 shows the organization of Section I.

Classification of Chinese Herbal Medicine Based on Improved LDA Algorithm Using Machine Olfaction

2012 ◽  
Vol 239-240 ◽  
pp. 1532-1536 ◽  
Author(s):  
De Han Luo ◽  
Ya Wen Shao
Keyword(s):  
Small Sample Size ◽  
Herbal Medicines ◽  
Small Sample ◽  
Fisher Criterion ◽  
Classification Rate ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Chinese Herbal ◽  
Machine Olfaction

Linear discriminant analysis (LDA) is a popular method among pattern recognition algorithms of machine olfaction. However, “Small Sample Size” (SSS) problem would occur while using LDA algorithm with traditional Fisher criterion if the within-class scatter matrix is singular. In this paper, maximum scatter difference (MSD) criterion and LDA were combined to solve SSS problem, so that three kinds of Chinese herbal medicines from different growing areas were accurately classified. At the same time, the classification result was enhanced. It works out that only a few samples of Anhui Atractylodes are classified incorrectly, however, the classification rate reaches 97.8%.

scholarly journals A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Zhicheng Lu ◽  
Zhizheng Liang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Small Sample Size ◽  
Classification Performance ◽  
Optimization Criterion ◽  
Small Sample ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Maximum Margin Criterion

Linear discriminant analysis has been widely studied in data mining and pattern recognition. However, when performing the eigen-decomposition on the matrix pair (within-class scatter matrix and between-class scatter matrix) in some cases, one can find that there exist some degenerated eigenvalues, thereby resulting in indistinguishability of information from the eigen-subspace corresponding to some degenerated eigenvalue. In order to address this problem, we revisit linear discriminant analysis in this paper and propose a stable and effective algorithm for linear discriminant analysis in terms of an optimization criterion. By discussing the properties of the optimization criterion, we find that the eigenvectors in some eigen-subspaces may be indistinguishable if the degenerated eigenvalue occurs. Inspired from the idea of the maximum margin criterion (MMC), we embed MMC into the eigen-subspace corresponding to the degenerated eigenvalue to exploit discriminability of the eigenvectors in the eigen-subspace. Since the proposed algorithm can deal with the degenerated case of eigenvalues, it not only handles the small-sample-size problem but also enables us to select projection vectors from the null space of the between-class scatter matrix. Extensive experiments on several face images and microarray data sets are conducted to evaluate the proposed algorithm in terms of the classification performance, and experimental results show that our method has smaller standard deviations than other methods in most cases.

AGGREGATION OF SPARSE LINEAR DISCRIMINANT ANALYSES FOR EVENT-RELATED POTENTIAL CLASSIFICATION IN BRAIN-COMPUTER INTERFACE

2013 ◽  
Vol 24 (01) ◽  
pp. 1450003 ◽  
Author(s):  
YU ZHANG ◽  
GUOXU ZHOU ◽  
JING JIN ◽  
QIBIN ZHAO ◽  
XINGYU WANG ◽  
...  
Keyword(s):  
Least Squares ◽  
Small Sample Size ◽  
Brain Computer Interface ◽  
Curse Of Dimensionality ◽  
Small Sample ◽  
Event Related Potential ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Training Samples ◽  
Discriminant Analyses

Two main issues for event-related potential (ERP) classification in brain–computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI.

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