Education of tobacco use prevention and cessation for dental professionals--a paradigm shift

The use of tobacco continues to be a substantial risk factor in the development and progression of oral cancer, periodontitis, implant failure and poor wound healing. Dental and dental hygiene education providers have made great advances towards the incorporation of tobacco education into their curricula in recent years. Unfortunately, however, both medical and dental education research has consistently reported schools providing only basic knowledge-based curricula that rarely incorporate more effective, behaviourally-based components affecting long-term change. The limited training of oral healthcare students, at least in part, is reflected in practising dental professionals continuing to report offering incomplete tobacco interventions. In order to prepare the next generation of oral healthcare providers, this paper proposes a paradigm shift in how tobacco use prevention and cessation (TUPAC) may be incorporated into existing curricula. It is suggested that schools should carefully consider: to what level of competency should TUPAC be trained in dental and dental hygiene schools; the importance of establishing rapport through good communication skills; the core knowledge level for TUPAC; suggested instructional and assessment strategies; the importance of continuing professional education for the enhancement of TUPAC.

Numerical model of the human cornea based on stromal microstructure: identification of mechanical properties for two age groups

The optical quality of the human eye mainly depends on the refractive performance of the cornea. The shape of the cornea is a mechanical balance between intraocular pressure and tissue intrinsic stiffness. Several surgical procedures in ophthalmology alter the biomechanics of the cornea to provoke local or global curvature changes for vision correction. Legitimated by the large number of surgical interventions performed every day, the demand for a deeper understanding of corneal biomechanics is rising to improve the safety of procedures and medical devices. The aim of our work is to propose a numerical model of corneal biomechanics, based on the stromal microstructure. Our novel anisotropic constitutive material law features a probabilistic weighting approach to model collagen fiber distribution as observed on human cornea by Xray scattering analysis (Aghamohammadzadeh et. al., Structure, February 2004). Furthermore, collagen cross-linking was explicitly included in the strain energy function. Results showed that the proposed model is able to successfully reproduce both inflation and extensiometry experimental data (Elsheikh et. al., Curr Eye Res, 2007; Elsheikh et. al., Exp Eye Res, May 2008). In addition, the mechanical properties calculated for patients of different age groups (Group A: 65-79 years; Group B: 80-95 years) demonstrate an increased collagen cross-linking, and a decrease in collagen fiber elasticity from younger to older specimen. These findings correspond to what is known about maturing fibrous biological tissue. Since the presented model can handle different loading situations and includes the anisotropic distribution of collagen fibers, it has the potential to simulate clinical procedures involving nonsymmetrical tissue interventions. In the future, such mechanical model can be used to improve surgical planning and the design of next generation ophthalmic devices.

Engineering Ethics: Looking Back, Looking Forward

The eight pieces constituting this Meeting Report are summaries of presentations made during a panel session at the 2011 Association for Practical and Professional Ethics (APPE) annual meeting held between March 3rd and 6th in Cincinnati. Lisa Newton organized the session and served as chair. The panel of eight consisted both of pioneers in the field and more recent arrivals. It covered a range of topics from how the field has developed to where it should be going, from identification of issues needing further study to problems of training the next generation of engineers and engineering-ethics scholars.

Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease

More than 1,000 susceptibility loci have been identified through genome-wide association studies (GWAS) of common variants; however, the specific genes and full allelic spectrum of causal variants underlying these findings have not yet been defined. Here we used pooled next-generation sequencing to study 56 genes from regions associated with Crohn's disease in 350 cases and 350 controls. Through follow-up genotyping of 70 rare and low-frequency protein-altering variants in nine independent case-control series (16,054 Crohn's disease cases, 12,153 ulcerative colitis cases and 17,575 healthy controls), we identified four additional independent risk factors in NOD2, two additional protective variants in IL23R, a highly significant association with a protective splice variant in CARD9 (P < 1 × 10(-16), odds ratio ≈ 0.29) and additional associations with coding variants in IL18RAP, CUL2, C1orf106, PTPN22 and MUC19. We extend the results of successful GWAS by identifying new, rare and probably functional variants that could aid functional experiments and predictive models.

Towards Energy Consumption Measurement in a Cloud Computing Wireless Testbed

The evolution of the Next Generation Networks, especially the wireless broadband access technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX), have increased the number of "all-IP" networks across the world. The enhanced capabilities of these access networks has spearheaded the cloud computing paradigm, where the end-users aim at having the services accessible anytime and anywhere. The services availability is also related with the end-user device, where one of the major constraints is the battery lifetime. Therefore, it is necessary to assess and minimize the energy consumed by the end-user devices, given its significance for the user perceived quality of the cloud computing services. In this paper, an empirical methodology to measure network interfaces energy consumption is proposed. By employing this methodology, an experimental evaluation of energy consumption in three different cloud computing access scenarios (including WiMAX) were performed. The empirical results obtained show the impact of accurate network interface states management and application network level design in the energy consumption. Additionally, the achieved outcomes can be used in further software-based models to optimized energy consumption, and increase the Quality of Experience (QoE) perceived by the end-users.

16S rRNA terminal restriction fragment length polymorphism for the characterization of the nasopharyngeal microbiota

A novel non-culture based 16S rRNA Terminal Restriction Fragment Length Polymorphism (T-RFLP) method using the restriction enzymes Tsp509I and Hpy166II was developed for the characterization of the nasopharyngeal microbiota and validated using recently published 454 pyrosequencing data. 16S rRNA gene T-RFLP for 153 clinical nasopharyngeal samples from infants with acute otitis media (AOM) revealed 5 Tsp509I and 6 Hpy166II terminal fragments (TFs) with a prevalence of >10%. Cloning and sequencing identified all TFs with a prevalence >6% allowing a sufficient description of bacterial community changes for the most important bacterial taxa. The conjugated 7-valent pneumococcal polysaccharide vaccine (PCV-7) and prior antibiotic exposure had significant effects on the bacterial composition in an additive main effects and multiplicative interaction model (AMMI) in concordance with the 16S rRNA 454 pyrosequencing data. In addition, the presented T-RFLP method is able to discriminate S. pneumoniae from other members of the Mitis group of streptococci, which therefore allows the identification of one of the most important human respiratory tract pathogens. This is usually not achieved by current high throughput sequencing protocols. In conclusion, the presented 16S rRNA gene T-RFLP method is a highly robust, easy to handle and a cheap alternative to the computationally demanding next-generation sequencing analysis. In case a lot of nasopharyngeal samples have to be characterized, it is suggested to first perform 16S rRNA T-RFLP and only use next generation sequencing if the T-RFLP nasopharyngeal patterns differ or show unknown TFs.

Translation from research to applications

The article summarizes the collective views expressed at the fourth session of the workshop Tissue Engineering-the Next Generation, which was devoted to the translation of results of tissue engineering research into applications. Ernst Hunziker described the paradigm of a dual translational approach, and argued that tissue engineering should be guided by the dimensions and physiological setting of the bodily compartment to be repaired. Myron Spector discussed collagen-glycosaminoglycan (GAG) scaffolds for musculoskeletal tissue engineering. Jeanette Libera focused on the biological and clinical aspects of cartilage tissue engineering, and described a completely autologous procedure for engineering cartilage using the patient's own chondrocytes and blood serum. Arthur Gertzman reviewed the applications of allograft tissues in orthopedic surgery, and outlined the potential of allograft tissues as models for biological and medical studies. Savio Woo discussed a list of functional tissue engineering approaches designed to restore the biochemical and biomechanical properties of injured ligaments and tendons to be closer to that of the normal tissues. Specific examples of using biological scaffolds that have chemoattractants as well as growth factors with unique contact guidance properties to improve their healing process were shown. Anthony Ratcliffe discussed the translation of the results of research into products that are profitable and meet regulatory requirements. Michael Lysaght challenged the proposition that commercial and clinical failures of early tissue engineering products demonstrate a need for more focus on basic research. Arthur Coury described the evolution of tissue engineering products based on the example of Genzyme, and how various definitions of success and failure can affect perceptions and policies relative to the status and advancement of the field of tissue engineering.

Tissue-transplant fusion and vascularization of myocardial microtissues and macrotissues implanted into chicken embryos and rats

Cell-based therapies and tissue engineering initiatives are gathering clinical momentum for next-generation treatment of tissue deficiencies. By using gravity-enforced self-assembly of monodispersed primary cells, we have produced adult and neonatal rat cardiomyocyte-based myocardial microtissues that could optionally be vascularized following coating with human umbilical vein endothelial cells (HUVECs). Within myocardial microtissues, individual cardiomyocytes showed native-like cell shape and structure, and established electrochemical coupling via intercalated disks. This resulted in the coordinated beating of microtissues, which was recorded by means of a multi-electrode complementary metal-oxide-semiconductor microchip. Myocardial microtissues (microm3 scale), coated with HUVECs and cast in a custom-shaped agarose mold, assembled to coherent macrotissues (mm3 scale), characterized by an extensive capillary network with typical vessel ultrastructures. Following implantation into chicken embryos, myocardial microtissues recruited the embryo's capillaries to functionally vascularize the rat-derived tissue implant. Similarly, transplantation of rat myocardial microtissues into the pericardium of adult rats resulted in time-dependent integration of myocardial microtissues and co-alignment of implanted and host cardiomyocytes within 7 days. Myocardial microtissues and custom-shaped macrotissues produced by cellular self-assembly exemplify the potential of artificial tissue implants for regenerative medicine.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Numerical modeling of the failure mechanisms in Si thin film anode for Li-ion batteries

In recent times, the demand for the storage of electrical energy has grown rapidly for both static applications and the portable electronics enforcing the substantial improvement in battery systems, and Li-ion batteries have been proven to have maximum energy storage density in all rechargeable batteries. However, major breakthroughs are required to consummate the requirement of higher energy density with lower cost to penetrate new markets. Graphite anode having limited capacity has become a bottle neck in the process of developing next generation batteries and can be replaced by higher capacity metals such as Silicon. In the present study we are focusing on the mechanical behavior of the Si-thin film anode under various operating conditions. A numerical model is developed to simulate the intercalation induced stress and the failure mechanism of the complex anode structure. Effect of the various physical phenomena such as diffusion induced stress, plasticity and the crack propagation are investigated to predict better performance parameters for improved design.

Quantum modeling of bioconjugated nanomaterials

In recent years, the bio-conjugated nanostructured materials have emerged as a new class of materials for the bio-sensing and medical diagnostics applications. In spite of their multi-directional applications, interfacing nanomaterials with bio-molecules has been a challenge due to somewhat limited knowledge about the underlying physics and chemistry behind these interactions and also for the complexity of biomolecules. The main objective of this dissertation is to provide such a detailed knowledge on bioconjugated nanomaterials toward their applications in designing the next generation of sensing devices. Specifically, we investigate the changes in the electronic properties of a boron nitride nanotube (BNNT) due to the adsorption of different bio-molecules, ranging from neutral (DNA/RNA nucleobases) to polar (amino acid molecules). BNNT is a typical member of III-V compounds semiconductors with morphology similar to that of carbon nanotubes (CNTs) but with its own distinct properties. More specifically, the natural affinity of BNNTs toward living cells with no apparent toxicity instigates the applications of BNNTs in drug delivery and cell therapy. Our results predict that the adsorption of DNA/RNA nucleobases on BNNTs amounts to different degrees of modulation in the band gap of BNNTs, which can be exploited for distinguishing these nucleobases from each other. Interestingly, for the polar amino acid molecules, the nature of interaction appeared to vary ranging from Coulombic, van der Waals and covalent depending on the polarity of the individual molecules, each with a different binding strength and amount of charge transfer involved in the interaction. The strong binding of amino acid molecules on the BNNTs explains the observed protein wrapping onto BNNTs without any linkers, unlike carbon nanotubes (CNTs). Additionally, the widely varying binding energies corresponding to different amino acid molecules toward BNNTs indicate to the suitability of BNNTs for the biosensing applications, as compared to the metallic CNTs. The calculated I-V characteristics in these bioconjugated nanotubes predict notable changes in the conductivity of BNNTs due to the physisorption of DNA/RNA nucleobases. This is not the case with metallic CNTs whose transport properties remained unaltered in their conjugated systems with the nucleobases. Collectively, the bioconjugated BNNTs are found to be an excellent system for the next generation sensing devices.

QUADRUPOLE LEVITATION OF PARTICLES IN A THERMODYNAMICALLY REALISTIC CLOUD ENVIRONMENT

Understanding clouds and their role in climate depends in part on our ability to understand how individual cloud particles respond to environmental conditions. Keeping this objective in mind, a quadrupole trap with thermodynamic control has been designed and constructed in order to create an environment conducive to studying clouds in the laboratory. The quadrupole trap allows a single cloud particle to be suspended for long times. The temperature and water vapor saturation ratio near the trapped particle is controlled by the flow of saturated air through a tube with a discontinuous wall temperature. The design has the unique aspect that the quadrupole electrodes are submerged in heat transfer fluid, completely isolated from the cylindrical levitation volume. This fluid is used in the thermodynamic system to cool the chamber to realistic cloud temperatures, and a heated section of the tube provides for the temperature discontinuity. Thus far, charged water droplets, ranging from about 30-70 microns in diameter have been levitated. In addition, the thermodynamic system has been shown to create the necessary thermal conditions that will create supersaturated conditions in subsequent experiments. These advances will help lead to the next generation of ice nucleation experiments, moving from hemispherical droplets on a substrate to a spherical droplet that is not in contact with any surface.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.