Bioactive ceramic coating on orthopedic implants for enhanced bone tissue integration

Tissue integration between bone and orthopedic implant is essential for implant fixation and longevity. An immunological response leads to fibrous encapsulation of metallic implants leading to implant instability and failure. Bioactive ceramics have the ability to directly bond to bone; however, they have limited mechanical strength for load bearing applications. Coating bioactive ceramics on metallic implant offers the exciting opportunity to enhance bone formation without compromising the mechanical strength of the implant. In the present study, we have developed a novel bioactive silica-calcium phosphate nanocomposite (SCPC) coating on medical grade Ti-6Al-4V orthopedic implant using Electrophoretic Deposition (EPD) and evaluated bone tissue response to the coated implant at a cellular level. The effect of SCPC composition and suspending medium pH on the zeta potential of three different SCPC formulations; SCPC25, SCPC50 and SCPC75 were analyzed. The average zeta potential of SCPC50 in pure ethanol was more negative than that of SCPC25 or SCPC75; however the difference was not statistically significant. Ti-6Al-4V discs were passivated, coated with SCPC50 (200 nm - 10 µm) and thermally treated at 600 - 800 oC to produce a coating thickness in the range of 43.1 ± 5.7 to 30.1 ± 4.6 µm. After treatment at 600, 700 and 800 oC, the adhesion strength at the SCPC50/Ti-6Al-4V interface was 42.6 ± 3.6, 44.7 ± 8.7 and 47.2 ± 4.3 MPa, respectively. XRD analyses of SCPC50 before and after EPD coating indicated no change in the crystallinity of the material. Fracture surface analyses showed that failure occurred within the ceramic layer or at the ceramic/polymer interface; however, the ceramic/metal interface was intact in all samples. The adhesion strength of SCPC50-coated substrates after immersion in PBS for 2 days (11.7 ± 3.9 MPa) was higher than that measured on commercially available hydroxyapatite (HA) coated substrates (5.5 ± 2.7 MPa), although the difference was not statistically significant. SEM - EDX analyses of SCPC50-coated Ti-6Al-4V pre- immersed in PBS for 7 days showed the formation of a Ca-deficient HA surface layer. Bone cells attached to the SCPC50-coated implants expressed significantly higher (p < 0.05) alkaline phosphatase activity (82.4 ± 25.6 nmoles p-NP/mg protein/min) than that expressed by cells attached to HA-coated or uncoated implants. Protein adsorption analyses showed that SCPC50-coated substrates adsorbed significantly more (p < 0.05) serum protein (14.9 ± 1.2 µg) than control uncoated substrates (8.9 ± 0.7 µg). Moreover, Western blot analysis showed that the SCPC50 coating has a high affinity for serum fibronectin. Protein conformation analyses by FTIR showed that the ratio of the area under the peak for amide I/amide II bands was significantly higher (p < 0.05) on the surface of SCPC50-coated substrate (5.0 ± 0.6) than that on the surface of the control uncoated substrates (2.2 ± 0.3). Moreover, ICP-OES analyses indicated that SCPC50- coated substrates withdrew Ca ions from, and released P and Si ions into, the tissue culture medium, respectively. In conjunction with the favorable protein adsorption and modifications in medium composition, MC3T3-E1 osteoblast-like cells attached to SCPC50-coated substrates expressed 10-fold higher level of mRNA encoding osteocalcin and had significantly higher production of osteopontin and osteocalcin proteins than cells attached to the uncoated Ti-6Al-4V substrate. In addition, osteoblast-like cells attached to the SCPC50-coated substrates produced significantly lower levels of the inflammatory and osteoclastogenic cytokines, IL-6, IL-12p40 and RANKL than those attached to uncoated Ti-6A1-4V. Surface topography analyses using AFM suggested that the SCPC50 particles deposit onto the metal surface in a manner that preferentially fills the grooves on the substrate created during substrate preparation. An increase in the surface roughness of the SCPC50-coated substrate from 217.8 ± 54.6 nm to 284.3 ± 37.3 nm was accompanied by enhanced material dissolution, reduced cell proliferation and poor actin cytoskeleton organization, which are characteristics typical of differentiating bone cells on bioactive ceramic surfaces. Results of the study demonstrate that bioactive SCPC50 can efficiently be coated on Ti-6Al-4V using EPD. Moreover, the in vitro bone cell response suggests that SCPC50-coating has the potential to enhance bone integration with orthopedic and maxillofacial implants while minimizing the induction of inflammatory bone cell responses

CHARACTERIZATION OF THE DYNAMIC PERFORMANCE OF MACHINE SPINDLES

Machine spindle dynamics and (axis of rotation) error motions may vary as a function of spindle speed due to gyroscopic effects, changes in bearing preload, centrifugal forces, and thermals effects. It is necessary to characterize these changes in order to fully define the spindle’s performance. In this research, two different aspects of spindle performance are considered: a) spindle dynamics; and b) spindle error (SE) motions. The objective is to simultaneously measure the (potential) changes in both the error motions and dynamic response with spindle speed. This work is motivated by the influence of spindle performance on machining operations. Machining instability (chatter) leads to poor surface finish, high rejection rates, rapid tool wear, and, potentially, spindle damage. Stable machining conditions may be identified using well-known milling process models. To do so, the dynamics of the tool-holder-spindle-machine assembly as reflected at the tool tip is required. Here, the dynamics of an artifact-spindle-machine combination are measured at the tip of a standard artifact when the spindle is rotating. Tests are conducted at different spindle speeds to capture the speed-dependent changes in the spindle dynamics. Receptance coupling substructure analysis (RCSA) is then applied to predict the tool point response for arbitrary tool-holder combination in the same spindle. RCSA is used to first decouple the artifact dynamics from the measured artifact-spindle-machine assembly dynamics (to isolate the spindle contributions) and then analytically couple the dynamics of a modeled tool-holder to the spindle-machine in order to predict the tool point frequency response function (FRF). A speed-dependent milling stability lobe diagram, which graphically depicts the allowable axial depth of cut as a function of spindle speed, is obtained by identifying the changes in tool point dynamics with spindle speed. Spindle error motions, which describe the variable position and orientation of the spindle axis as a function of the rotation angle, can affect machined surface quality. Non- contact sensors (such as capacitance gages) may be used to measure the SE motions while the spindle is rotating. A multi-probe error separation method is used to accurately isolate the SE motions and the artifact form error. Tests are repeated at different spindle speeds to examine the associated effects. Together, the identification of the speed- dependent SE motions and tool point FRF will enable an improved capability to predict the milling performance for a given tool-holder-spindle-machine combination. In this research, the speed-dependent spindle dynamics and the SE motions for three different Haas TM1 machine spindles were studied. At a spindle speed of 3800 rpm, the critical stable axial depth of cut predicted using the stationary tool point FRFs was 6 mm while that predicted using the speed-dependent FRFs was 10 mm. Stable machining was observed at an axial depth of cut of 9 mm at a spindle speed of 3800 rpm. The results showed that incorporating the changing dynamics of the spindle in machining stability models improved the ability to predict chatter. Further, the dynamics and error motions of an NSK HES-500 high speed spindle were also measured

SPECTRAL ANALYSIS OF RICH NETWORK TOPOLOGY IN SOCIAL NETWORKS

Social networks have received much attention these days. Researchers have de- veloped different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few theoretical and systematical studies to support their findings. In this dissertation, we conduct an in-depth theoretical study to show the close relationship between algebraic spectral properties of the adjacency matrix and vari- ous patterns in a broad range of social networks such as friendship networks, alliance and war networks, and distrusted networks. In our framework, we apply matrix per- turbation theory and approximate the eigenvectors of real graphs by those of the ideal cases. Our theoretical results show that the principal eigenvectors capture the structure of major communities and exhibit them as orthogonal lines/clusters rotated with certain angles from canonical axes. Our results also show that the minor eigen- vectors with skew distributions in values capture weak or subtle signals hidden in local communities. We utilize our theoretical results to develop algorithms for several problems in social network analysis including community partition, anomaly detec- tion and privacy preserving social network reconstruction. Empirical evaluations on various synthetic data and real-world social networks validate our theoretical findings and show the effectiveness of our algorithms. In a nutshell, we theoretically study the patterns in the adjacency spectral space as well as conditions for their existence and explore the application of the spectral properties of the adjacency matrix in different tasks of social network analysis

Infrared photocurrent spectroscopy of deep levels in magnesium-doped gallium nitride and aluminum gallium nitride

Recently, there is an increasing demand for III-nitride semiconductor based optoelectronic devices, and especially for ultraviolet (UV) and Deep UV sources and sensors; however, the development and the performance of such devices is fundamentally limited by low carrier concentration, especially in p-type GaN and its alloys with aluminum, p-type AlxGa1-xN. Common acceptors such as magnesium (Mg), which function adequately in GaN, are often too deep in AlGaN alloys to allow significant acceptor ionization at room temperature. Various strategies such as short period superlattices are often incorporated into device architectures in order to enhance carrier levels in p-AlGaN. In this work, IR photocurrent spectroscopy in Mg doped GaN, and Mg-doped AlxGa1-xN (0.15 < x < 0.52) was conducted by means of a YAG-pumped OPO/OPA system tunable from 250 meV to 1.75 eV with the goal of observing and identifying energy levels associated with acceptor atoms of Mg in GaN and AlxGa1-xN. Infrared photocurrent spectra are presented from a variety of GaN and AlxGa1-xN test structures. Non-zero background response is associated with shallow extrinsic impurities and/or a continuum of shallow levels, photocurrent response peaks observed and associated with deep level donors. No evidence of acceptor ionization associated with Mg in magnesium-doped GaN and AlxGa1-xN is observed. A number of deep levels are observed in photocurrent spectra, including several 400 meV in GaN and low aluminum alloys, and one around 800 meV in higher aluminum alloys. Thermal analysis of Mg:GaN photocurrent data is consistent with the deep levels being electron donors. Finally, the effects of IR radiation on the UV optical output of forward biased commercial 365nm UV LEDs is investigated and reported

MICROSTRUCTURAL MODELING DURING MULTI-PASS ROLLING OF A NICKEL-BASE SUPERALLOY

Microstructure present at the end of rolling and cooling operations controls the product properties. Therefore, control of grain size is an important characteristic in any hot-working. The narrow temperature range for hot working of Alloy 718 makes the grain size control more difficult. In the current work, a systematic nu- merical approach to predict the microstructure of Alloy 718 during multi-pass rolling is developed. This approach takes into account the severe deformation that takes place during each pass and also the possible reheating between passes. In order to predict the grain size at the end of rolling process, microstructural processes such as dynamic recrystallization (DRX), metadynamic recrystallization (MDRX), and static grain growth need to be captured at every deformation step for superalloys. Empirical relationships between the average grain size from various microstructural processes and the macroscopic variables such as temperature (T ), effective strain (e ¯) and strain rate (e ¯ ?) form the basis for the current work. The empirical relationships considered in this work are based on Avrami equations and utilize data taken from various forging analyses. The macroscopic variables are calculated using the Finite Element Method (FEM) by modeling the rolling process as a creeping flow problem. FEM incorporates a mesh re-zoning algorithm that enables the analysis to continue for several passes. A two-dimensional transient thermal analysis is carried out between passes that can capture the MDRX and/or static grain growth during the microstructural evolution. The microstructure prediction algorithm continuously updates two families of grains, namely, the recrystallized family and strained family at the start of deformation in any given pass. In addition, the algorithm calculates various subgroups within these two families at every deformation step within a pass. As the material undergoes deformation between the rolls, recrystallization equations are invoked depending on critical strain and strain rate conditions that are characteristics of Alloy 718. This approach predicts the microstructural evolution based on recrystallization kinetics and static grain growth only. Precipitation of phases such as ?', ?'' and d are not considered. Modeling this complex precipitation is difficult and requires a more detailed understanding than is presently available. Nevetheless, comparisons of the grain sizes from the proposed numerical models with experimental results for 16-stand rolling process show very good agreement

Level based sampling techniques for energy conservation in large scale wireless sensor networks

As the size and node density of wireless sensor networks (WSN) increase,the energy conservation problem becomes more critical and the conventional methods become inadequate. This dissertation addresses two different problems in large scale WSNs where all sensors are involved in monitoring,but the traditional practice of periodic transmissions of observations from all sensors would drain excessive amount of energy. In the first problem,monitoring of the spatial distribution of a two dimensional correlated signal is considered using a large scale WSN. It is assumed that sensor observations are heavily affected by noise. We present an approach that is based on detecting contour lines of the signal distribution to estimate the spatial distribution of the signal without involving all sensors in the network. Energy efficient algorithms are proposed for detecting and tracking the temporal variation of the contours. Optimal contour levels that minimize the estimation error and a practical approach for selection of contour levels are explored. Performance of the proposed algorithm is explored with different types of contour levels and detection parameters. In the second problem,a WSN is considered that performs health monitoring of equipment from a power substation. The monitoring applications require transmissions of sensor observations from all sensor nodes on a regular basis to the base station,which is very costly in terms of communication cost. To address this problem,an efficient sampling technique using level-crossings (LCS) is proposed. This technique saves communication cost by suppressing transmissions of data samples that do not convey much information. The performance and cost of LCS for several different level-selection schemes are investigated. The number of required levels and the maximum sampling period for practical implementation of LCS are studied. Finally,in an experimental implementation of LCS with MICAzmote,the performance and cost of LCS for temperature sensing with uniform,logarithmic and a combined version of uniform and logarithmically spaced levels are compared with that using periodic sampling

Monte-Carlo tree search with heuristic knowledge: A novel way in solving capturing and life and death problems in Go

Monte-Carlo (MC) tree search is a new research field. Its effectiveness in searching large state spaces, such as the Go game tree, is well recognized in the computer Go community. Go domain- specific heuristics and techniques as well as domain-independent heuristics and techniques are sys- tematically investigated in the context of the MC tree search in this dissertation. The search extensions based on these heuristics and techniques can significantly improve the effectiveness and efficiency of the MC tree search. Two major areas of investigation are addressed in this dissertation research: I. The identification and use of the effective heuristic knowledge in guiding the MC simulations, II. The extension of the MC tree search algorithm with heuristics. Go, the most challenging board game to the machine, serves as the test bed. The effectiveness of the MC tree search extensions is demonstrated through the performances of Go tactic problem solvers using these techniques. The main contributions of this dissertation include: 1. A heuristics based Monte-Carlo tactic tree search framework is proposed to extend the standard Monte-Carlo tree search. 2. (Go) Knowledge based heuristics are systematically investigated to improve the Monte-Carlo tactic tree search. 3. Pattern learning is demonstrated as effective in improving the Monte-Carlo tactic tree search. 4. Domain knowledge independent tree search enhancements are shown as effective in improving the Monte-Carlo tactic tree search performances. 5. A strong Go Tactic solver based on proposed algorithms outperforms traditional game tree search algorithms. The techniques developed in this dissertation research can benefit other game domains and ap- plication fields

A COMMUNICATION FRAMEWORK FOR MULTIHOP WIRELESS ACCESS AND SENSOR NETWORKS: ANYCAST ROUTING & SIMULATION TOOLS

The reliance on wireless networks has grown tremendously within a number of varied application domains, prompting an evolution towards the use of heterogeneous multihop network architectures. We propose and analyze two communication frameworks for such networks. A first framework is designed for communications within multihop wireless access networks. The framework supports dynamic algorithms for locating access points using anycast routing with multiple metrics and balancing network load. The evaluation shows significant performance improvement over traditional solutions. A second framework is designed for communication within sensor networks and includes lightweight versions of our algorithms to fit the limitations of sensor networks. Analysis shows that this stripped down version can work almost equally well if tailored to the needs of a sensor network. We have also developed an extensive simulation environment using NS-2 to test realistic situations for the evaluations of our work. Our tools support analysis of realistic scenarios including the spreading of a forest fire within an area, and can easily be ported to other simulation software. Lastly, we us our algorithms and simulation environment to investigate sink movements optimization within sensor networks. Based on these results, we propose strategies, to be addressed in follow-on work, for building topology maps and finding optimal data collection points. Altogether, the communication framework and realistic simulation tools provide a complete communication and evaluation solution for access and sensor networks

Using embedded computer-assisted instruction to teach science to students with Autism Spectrum Disorders

The need for promoting scientific literacy for all students has been the focus of recent education reform resulting in the rise of the Science Technology, Engineering, and Mathematics movement. For students with Autism Spectrum Disorders and intellectual disability, this need for scientific literacy is further complicated by the need for individualized instruction that is often required to teach new skills, especially when those skills are academic in nature. In order to address this need for specialized instruction, as well as scientific literacy, this study investigated the effects of embedded computer-assisted instruction to teach science terms and application of those terms to three middle school students with autism and intellectual disability. This study was implemented within an inclusive science classroom setting. A multiple probe across participants research design was used to examine the effectiveness of the intervention. Results of this study showed a functional relationship between the number of correct responses made during probe sessions and introduction of the intervention. Additionally, all three participants maintained the acquired science terms and applications over time and generalized these skills across materials and settings. The findings of this study suggest several implications for practice within inclusive settings and provide suggestions for future research investigating the effectiveness of computer-assisted instruction to teach academic skills to students with Autism Spectrum Disorders and intellectual disability

RECIPROCAL REGULATION OF MYC AND MICRORNA MIR-308 DURING DROSOPHILA EMBRYOGENESIS

Myc is a conserved transcription factor with a role in the regulation of genes that are involved in growth and development. The abundance of Myc protein in the cells must be exquisitely controlled to avoid growth abnormalities caused by too much or too little Myc. An intriguing mode of regulation exists in which overabundance of Myc protein triggers a negative feedback regulation that leads to its abundance. In this study, I illustrate a mechanism for dMyc negative feedback regulation in Drosophila embryogenesis. I show that Drosophila Myc protein (dMyc) binds to the microRNA miR-308 locus and increases its expression. An increase in miR-308 levels leads to destabilization of dMyc mRNA and reduced dMyc protein levels. In vivo knockdown of miR-308 confirmed constant regulation of dMyc levels by miR-308 in embryos. My results also show that this regulatory loop is crucial for maintaining appropriate dMyc levels and normal development. Perturbation of the loop, either by elevated miR-308 or elevated dMyc, caused lethality. Combining elevated levels of both, therefore restoring balance between mir-308 and dMyc levels, resulted in suppression of lethality. These results reveal a sensitive feedback mechanism that is crucial to prevent the pathologies caused by abnormal levels of dMyc. Moreover, I show that the cross-regulation of dMyc and miR-308 has a role in regulation of dMyc target genes. In the second part of this study, I show that dMyc localizes in histone locus bodies during replication. The work that I describe here began with an observation of unexpected, punctate spots of Myc protein in certain regions of Drosophila embryos. I investigated the identity of these puncta and demonstrate that Myc is co-localized with coilin, a marker for Cajal Bodies (CBs), and Lsm11, a marker for Histone Locus bodies (HLBs), in embryos, larvae and ovaries. Using the MPM-2 antibody, I show that Myc’s association with HLBs occurs only during replication in both endocycling and mitotic cells. These results reveal a novel role for Myc in replication-dependent histone mRNA production and processing