Diversity of Site-Specific Microbes of Occlusal and Proximal Lesions in Severe- Early Childhood Caries (S-ECC)

Background: Severe-early childhood caries (S-ECC) is a global problem of significant concern, commonly manifest as a dentinal lesion on the occlusal and proximal surfaces of the affected deciduous dentition. Although there are major ecological differences between these two niches, it is unclear whether these are reflected in the composition of their dysbiotic cariogenic microbiome. Therefore, we compared the compositional differences in the microbiota of occlusal and proximal caries lesions in S-ECC. Methods: Deep-dentine caries samples (19-occlusal and 19-proximal) from asymptomatic primary molars of children with S-ECC (n=19) belonging to caries-code 5/6, according to ICDAS classification, were evaluated. Employing two primer pools, we amplified and compared the bacterial 16S rRNA gene sequences of the seven hypervariable regions (V2 to V4 and V6 to V9) using a next-generation sequencing based assay.Results: Bray-Curtis dissimilarity data indicated that occlusal lesions had a more homogeneous microbial community structure than the proximal lesions with significant compositional differences at species level (p=0.01; R-value of 0.513). Together, the occlusal and proximal niches harbored 263 species, of which 202 (76.8%) species were common to both locales, while 49 (18.6%) and 12 (4.6%) disparate species were exclusively isolated from the proximal and occlusal niches, respectively. The most commonly found genera at both locales included Streptococcus, Prevotella, and Lactobacillus, with 33, 27, and 22 species each, respectively. In addition, Streptococcus mutans predominated in the proximal cavities (p≤0.05), as opposed to Atopobium parvulum (p=0.01) in the occlusal niches, while Vellonella alcalescens was present in similar proportions in both habitats (p≥0.05). Conclusions: Distinct differences between the caries microbiota of occlusal and proximal caries in S-ECC exist. The former niche appears to provide a habitat for a more homogeneous growth of communal microbiota than the latter. This may be due to the conditions prevalent in relatively quiescent inter-proximal regions, as opposed to the occlusal regions exposed to the ebb and flow of salivary and masticatory forces, and/or the anatomical and structural differences in the two locales. The clinical implications of these findings in terms of the rate and severity of caries progression remain to be determined.

Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments

Assessing market structure by deriving a brand positioning map and segmenting customers is essential for supporting brand-related marketing decisions. We propose adaptive multidimensional scaling (ADMDS) for simultaneously deriving a brand positioning map and market segments using customer data on cognitive decision sets and brand dissimilarities. In ADMDS, the judgment task is adapted to the individual customer where dissimilarity judgments are collected only for those brands within a customers’ awareness set. Thus, respondent fatigue and unfamiliarity with the brands are circumvented thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived from them. Estimation of the ADMDS model results in a spatial map in which the brands and derived segments of customers are jointly represented as points. The closer a brand is positioned to a segment’s ideal brand, the higher the probability that the brand is considered and chosen. An assumption underlying this model representation is that brands within a customers’ consideration set are relatively similar. In an experiment with 200 respondents and 4 product categories, this assumption is validated. We illustrate adaptive multidimensional scaling model on commercial data for 20 midsize car brands evaluated by 212 members of an on-line consumer panel. Potential applications of the method and future research opportunities are discussed.

Performance evaluation of bearing degradation based on stationary wavelet decomposition and extra trees regression

Purpose In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation. Design/methodology/approach The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health. Findings Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression. Originality/value The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.

Unfolding Models for Asymmetric Dissimilarity Data With External Information Based on Path Structures

This article contains asymmetric dissimilarity data which is observed in various situations. In asymmetric dissimilarity data, dissimilarity from subject i to j and from subject j to i are not the same necessarily. Asymmetric multidimensional scaling (AMDS) is a visualization method for describing the asymmetric relations between subjects, given asymmetric dissimilarity data for subjects. It is sure that AMDS is a useful tool for interpreting the asymmetric relation, however, existing AMDS cannot be considered for the external information, even if the external information of the same subjects for the asymmetric dissimilarity data is given. If the estimated coordinates can be interpreted from the loading matrix for the external information like principal component analysis (PCA), the AMDS become more useful. This is because we can interpret the relation between the estimated asymmetries and the factors of the external information on the low dimensions. In this article, we proposed new AMDS with external information. In addition to that, the proposed method can consider the path structure for variables like SEM.