Fundamentals of low-cost aerosol sensor design and operation.

Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor's internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols.

Immunoglobulin gene expression profiles and microbiome characteristics in periodontitis in nonhuman primates.

Colonization of mucosal tissues throughout the body occurs by a wide array of bacteria in the microbiome that stimulate the cells and tissues, as well as respond to changes in the local milieu. A feature of periodontitis is the detection of adaptive immune responses to members of the oral microbiome that show specificity and changes with disease and treatment. Thus, variations in antibody responses are noted across the population and affected by aging, albeit, data are still unclear as to how these differences relate to disease risk and expression.

Variations in temporal trends in non-traumatic dental condition related emergencies.

Objective: The objective of the study was to investigate temporal trends in non-traumatic dental condition (NTDC) related emergency visits at Emergency Department (ED), urgent care (UC), and at a Federally Qualified Health Center (FQHC) that providing dental services to a mid-sized rural community.

Methods: Temporal trends over a 9-year period (2008-2016) in NTDC rates at ED, UC, FQHC and in a region around the FQHC were determined. Statistically significant changes (α = 0.

Oral Microbiome and Gingival Gene Expression of Inflammatory Biomolecules With Aging and Periodontitis.

Although data describe the presence and increase of inflammatory mediators in the local environment in periodontitis vs. health in humans, details regarding how these responses evolve in the transition from health to disease, changes during disease progression, and features of a resolved lesion remain unknown. This study used a nonhuman primate model of ligature-induced periodontitis in young, adolescent, adult, and aged animals to document features of inflammatory response affected by age.

Transcriptomic phases of periodontitis lesions using the nonhuman primate model.

We used a nonhuman primate model of ligature-induced periodontitis to identify patterns of gingival transcriptomic after changes demarcating phases of periodontitis lesions (initiation, progression, resolution). A total of 18 adult Macaca mulatta (12-22 years) had ligatures placed (premolar, 1st molar teeth) in all 4 quadrants. Gingival tissue samples were obtained (baseline, 2 weeks, 1 and 3 months during periodontitis and at 5 months resolution).

Variations in Schedule III prescription patterns in a Medicaid population pre- and post-policy.

The present study investigated variations in patient movement patterns between prescribers before and after House Bill 1 (HB1) implementation in Kentucky using network abstractions (PPN: prescriber-prescriber networks) from a one-month cross-sectional Schedule III prescription data in a Medicaid population. Network characteristics such as degree centrality distribution of PPN was positively skewed and revealed Dental Practitioners to be the highly connected specialty with opioid analgesic hydrocodone-acetaminophen to be the most commonly prescribed drug. Taxonomy enrichment of the prescriber specialties in PPN using chi-square test revealed a reduction in the enriched taxonomies Post-HB1 compared to Pre-HB1 with Dental practitioners being constitutively enriched (p < 0.

Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks.

Surrogate testing techniques have been used widely to investigate the presence of dynamical nonlinearities, an essential ingredient of deterministic chaotic processes. Traditional surrogate testing subscribes to statistical hypothesis testing and investigates potential differences in discriminant statistics between the given empirical sample and its surrogate counterparts. The choice and estimation of the discriminant statistics can be challenging across short time series.

Network Abstractions of Prescription Patterns in a Medicaid Population.

Understanding prescription patterns have relied largely on aggregate statistical measures. Evidence of doctor- shopping, inappropriate prescribing, drug diversion and patient seeking prescription drugs across multiple prescribers demand understanding the concerted working of prescribers and prescriber communities as opposed to treating them as independent entities. We model potential associations between prescribers as prescriber-prescriber network (PPN) and subsequently investigate its properties across Schedule II, III, IV drugs in a single month in a Medicaid population.

Biologic modelling of periodontal disease progression.

Aim: To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression.

Material And Methods: This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis. The primary outcome was detection of sites with progressing attachment loss sites over 6 months in patients who received scaling and root planing or oral hygiene only.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery.

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources.

The periodontal war: microbes and immunity.

Maintenance of periodontal health or transition to a periodontal lesion reflects the continuous and ongoing battle between the vast microbial ecology in the oral cavity and the array of resident and emigrating inflammatory/immune cells in the periodontium. This war clearly signifies many 'battlefronts' representing the interface of the mucosal-surface cells with the dynamic biofilms composed of commensal and potential pathogenic species, as well as more recent knowledge demonstrating active invasion of cells and tissues of the periodontium leading to skirmishes in connective tissue, the locality of bone and even in the local vasculature. Research in the discipline has uncovered a concerted effort of the microbiome, using an array of survival strategies, to interact with other bacteria and host cells.

An ensemble predictive modeling framework for breast cancer classification.

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size.

Integrated biomarker profiling of smokers with periodontitis.

Background: In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups.

Aim: The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers).

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles.

Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient-specific variations within the disease groups. Such variations are ubiquitous and manifests across multiple scales with varying resolution.

An informatics research agenda to support precision medicine: seven key areas.

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM.

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis.

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM).

Targeted salivary biomarkers for discrimination of periodontal health and disease(s).

Unlabelled: Generally, clinical parameters are used in dental practice for periodontal disease, yet several drawbacks exist with the clinical standards for addressing the needs of the public at large in determining the current status/progression of the disease, and requiring a significant amount of damage before these parameters can document disease. Therefore, a quick, easy and reliable method of assessing and monitoring periodontal disease should provide important diagnostic information that improves and speeds treatment decisions and moves the field closer to individualized point-of-care diagnostics.

Objective: This report provides results for a saliva-based diagnostic approach for periodontal health and disease based upon the abundance of salivary analytes coincident with disease, and the significant progress already made in the identification of discriminatory salivary biomarkers of periodontitis.

3D tumor tissue analogs and their orthotopic implants for understanding tumor-targeting of microenvironment-responsive nanosized chemotherapy and radiation.

Unlabelled: An appropriate representation of the tumor microenvironment in tumor models can have a pronounced impact on directing combinatorial treatment strategies and cancer nanotherapeutics. The present study develops a novel 3D co-culture spheroid model (3D TNBC) incorporating tumor cells, endothelial cells and fibroblasts as color-coded murine tumor tissue analogs (TTA) to better represent the tumor milieu of triple negative breast cancer in vitro. Implantation of TTA orthotopically in nude mice, resulted in enhanced growth and aggressive metastasis to ectopic sites.

Social network analysis to assess the impact of the CTSA on biomedical research grant collaboration.

Success of the Clinical Translational Science Award (CTSA) program implicitly demands team science efforts and well-orchestrated collaboration across the translational silos (T1-T4). Networks have proven to be useful abstractions of research collaborations. Networks provide novel system-level insights and exhibit marked changes in response to external interventions, making them potential evaluation tools that complement more traditional approaches.

Impact of noise on molecular network inference.

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms.

Identifying significant edges in graphical models of molecular networks.

Objective: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations.

A neural network model to translate brain developmental events across mammalian species.

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially useful since the distribution of the event timing data is skewed with a majority of events documented only across a few selected species.

Evidence of community structure in biomedical research grant collaborations.

Recent studies have clearly demonstrated a shift towards collaborative research and team science approaches across a spectrum of disciplines. Such collaborative efforts have also been acknowledged and nurtured by popular extramurally funded programs including the Clinical Translational Science Award (CTSA) conferred by the National Institutes of Health. Since its inception, the number of CTSA awardees has steadily increased to 60 institutes across 30 states.

A selective voting convex-hull ensemble procedure for personalized medicine.

Genes work in concert as a system as opposed to independent entities and mediate disease states. There has been considerable interest in understanding variations in molecular signatures between normal and disease states. However, a majority of techniques implicitly assume homogeneity between samples within a given group and use a fixed set of genes in discerning the groups.

Temporal evolution of biomedical research grant collaborations across multiple scales--a CTSA baseline study.

The evolution of biomedical research grant collaborations (BRGC) across time (2006, 2009) and hierarchically related scales (Staff, Department) at the University of Arkansas for Medical Sciences (UAMS) is investigated using network abstractions. This baseline study is a part of the Clinical Translational Science Award (CTSA) efforts in promoting team science and exploring network science approaches for CTSA evaluation. The BRGC data were retrieved from the internally developed grants management system (Automated Research Information Administrator, ARIA).