Statistics for Spatial Data.

Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95

In the era of applied meta-omics and personalized medicine, the oral microbiome is a valuable asset. From biomarker discovery to being a powerful source of therapeutic targets and to presenting an opportunity for developing non-invasive approaches to health care, it has become clear that oral microbes may hold the answer for understanding disease, even beyond the oral cavity. Although our understanding of oral microbiome diversity has come a long way in the past 50 years, there are still many areas that need to be fine-tuned for better risk assessment and diagnosis, especially in early developmental stages of human life. Here, we discuss the factors that impact development of the oral microbiome and explore oral markers of disease, with a focus on the early oral cavity. Our ultimate goal is to put different experimental and methodological views into perspective for better assessment of early oral and systemic disease at an early age and discuss how oral microbiomes-at the community level-could provide improved assessment in individuals and populations at risk.

/pdf/the-oral-microbiome-of-children-development-disease-and-ysq9rlygak.pdf

The Oral Microbiome of Children: Development, Disease, and Implications Beyond Oral Health

Dental disease in childhood has the potential to inform about food availability, social status, and feeding practices, in addition to contributing to a child’s overall health status. This paper presents the first comprehensive overview of carious lesion frequencies in 433 nonadults (1-17 years), and 6283 erupted permanent and deciduous teeth from 15 urban and rural Romano-British settlements. Pooled deciduous and permanent caries rates were significantly higher in major urban sites (1.8%) compared to rural settlements (0.4%), with children from urban sites having significantly higher lesion rates in the deciduous dentition (3.0%), and in
younger age groups with mixed dentitions. The differences in dental caries between urban and rural populations suggest disparities in maternal oral health, early childhood feeding practices, food preparation and access to refined carbohydrates. A richer, perhaps more ‘Roman’, cuisine was eaten in the urban settlements, as opposed to a more modest diet in the countryside. The effect of early childhood stress on caries frequency was explored using evidence for enamel hypoplasia. Co-occurrence of caries and enamel hypoplasia was highest in the major urban cohort (5.8%) and lowest in the rural sample (1.3%), suggesting that environmental stress was a contributing factor to carious lesion development in Romano-British urban children.

http://journal.dentalanthropology.org/index.php/jda/article/download/32/17

Dental Caries as a Measure of Diet, Health, and Difference in Non-Adults from Urban and Rural Roman Britain

The pathogen Phytophthora capsici causes lesions on the crown, stem, and leaves of bell pepper, and rapidly causes the plant to die. The spatial patterns of disease in an agricultural field contain information about pathogen dispersal mechanisms and can be useful for developing methods of control of disease. Soil water content, soil pathogen population density, and disease incidence data were collected on a 20 x 20 grid in two naturally infested commercial bell pepper fields. In one field the initial pattern of disease closely matched the soil water content pattern and disease developed in areas where the pathogen population levels were high. In the other field no such correspondence was obvious from maps of disease and soil water content. The auto logistic model is a flexible model for predicting presence or absence of disease based on soil water content and soil pathogen population, while taking spatial correlation into account. In the autologistic model the log odds of disease in a particular quadrat are modeled as a linear combination of disease in neighboring quadrats and the soil variables. Neighboring quadrats can be defined as adjacent quadrats within a row, quadrats in adjacent rows, quadrats two rows away, and so forth. The regression coefficients give estimates of the increase in odds of disease if neighbors within a row or in adjacent rows show disease symptoms; thus we obtain information about the degree of spread in different directions. The coefficients for the soil variables give estimates of the increase in odds of disease as soil water content or pathogen population density increase. In this problem, soil water content is also highly correlated over quadrats. This introduces a kind of collinearity between water content and the disease in neighboring quadrats, making estimation and interpretation of the parameters of the auto logistic model more difficult. We discuss fitting and evaluating the autologistic model when the covariates are themselves spatially correlated. Additional keywords: spatial correlation, disease incidence, Markov random field, multidimensional binary data, pseudolikelihood estimation.

/pdf/autologistic-model-of-spatial-pattern-of-phytophthora-461gzxhlh6.pdf

Autologistic model of spatial pattern of phytophthora epidemic in bell pepper: effects of soil variables ondisease presence

The autologistic model, first introduced by Besag, is a popular tool for analyzing binary data in spatial lattices. However, no investigation was found to consider modeling of binary data clustered in uncorrelated lattices. Owing to spatial dependency of responses, the exact likelihood estimation of parameters is not possible. For circumventing this difficulty, many studies have been designed to approximate the likelihood and the related partition function of the model. So, the traditional and Bayesian estimation methods based on the likelihood function are often time-consuming and require heavy computations and recursive techniques. Some investigators have introduced and implemented data augmentation and latent variable model to reduce computational complications in parameter estimation. In this work, the spatially correlated binary data distributed in uncorrelated lattices were modeled using autologistic regression, a Bayesian inference was developed with contribution of data augmentation and the propos...

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children

A model for analyzing spatially correlated binary data clustered in uncorrelated lattices

Effects of Adjacent Teeth on Caries Status of a Deciduous Tooth in 3-5 Years-Old Children

Comparison of the Effectiveness of Cognitive-behavioral Model Implementation by Peers and Nurses on Anxiety in Patients with Type 2 Diabetes

It is widely accepted that any interventional treatment, such as surgery, requires a precise and predetermined treatment plan. Moreover, conventional images do not allow for the presentation of all canal dimensions, and patients concepts of beauty and their expectations are greater for the premaxilla region. Therefore, the use of three-dimensional images is essential in surgical procedures, such as pathological lesions or implant placement surgeries.
In cone-beam computed-tomography images, the nasopalatine canal was examined in sagittal, coronal, and axial planes. The diameters of nasopalatine and incisive foramina were separately measured, and the length of the nasopalatine canal was found by measuring the distance between the mid-levels of the nasopalatine foramen and incisive foramen. The shape of the canal was assessed in the sagittal and coronal planes in the mid-level of the canal. In addition, the shape of the canal and posterior borders were examined in the mid-level of the canal in the axial plane. The nasopalatine angle was measured as an anterior angle between the long axis of the canal and the hard palate. The number of canals in the midline and openings in each plane was also counted.
The nasopalatine canal in the sagittal plane was classified into six groups: conical (33.2%), cylindrical (25.6%), hourglass (24.7%), funnel-shaped (9.8%), reverse-cone (4.3%), and spindle (2.4%). In the coronal plane, the shape of the canal was assigned to three categories: single channel (59.2%), Y-shaped (31.2%), and dual-channel (9.6%), and the posterior border of the nasopalatine canal was classified into four groups: U-shaped (42.5%), V-shaped (37.2%), reverse-V-shaped (154%), and Y-shaped (4.9%). Finally, in the axial plane, the canal shape was classified into four groups: round (40.5%), oval (31.1%), heart-shaped (21.3%), and triangle-shaped (7%).
The use of three-dimensional images should be strongly considered in all surgical interventions involving the nasopalatine canal, such as dental implant placement since any error in surgical interventions will bring about serious consequences due to higher aesthetic expectations for the anterior maxillary region. Due to the lack of correct diagnosis of canal morphology in conventional images, such as periapical and panoramic radiography, it seems necessary to use three-dimensional radiography when performing surgical interventions in this region.

Anatomical characteristics of nasopalatine canal using cone beam computed tomography images

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Solaiman Afroughi

Author Tools

Chat about Author