MEDIUM-CHAIN-LENGTH POLY(3-HYDROXYALKANOATE) NANOPARTICLE SUSPENSIONS

The main goal of this thesis was to prepare medium-chain-length poly-3-hydroxyalkanoate (mcl-PHA) nanoparticle suspensions at high solids content (≥ 10 % w/v). A two-stage emulsification-solvent evaporation process was employed to produce poly-3-hydroxydecanoate (PHD) suspensions. The formulation and processing conditions including ultrasonication time and amplitude, selection of solvent, and selection of surfactants and their concentrations were investigated to make concentrated suspensions (10 and 30 % (w/v)) of PHD with particles less than 300 nm. Among the ionic surfactants tested to stabilize the suspension, the anionic, sodium dodecyl sulphate (SDS), and the cationic, dodecyltrimethylammonium bromide (DTAB) surfactants produced the smallest particle sizes (~100 nm). However, more stabilized nanoparticles were obtained when the ionic surfactant, SDS, was combined with any of the non-ionic surfactants tested, with polyoxyethylene octyl phenyl ether (Triton X-100) or polyoxyethylene (20) sorbitan monooleate (Tween 80) resulting in a slight increase in zeta potential over 30 days while the zeta potential with other non-ionic surfactants decreased. Mcl-PHA containing 11 and 18 % of carboxyl groups was synthesized via free radical addition reaction of 11-mercaptoundecanoic acid to the pendant double bonds of unsaturated poly-3-hydroxynonanoate (PHNU). Colloidal suspensions prepared by ultrasonication needed a surfactant to maintain stability, even at 0.4 % solids of mcl-PHA containing 11 % carboxylation (PHNC-1) unlike the stable suspensions prepared without surfactants by the titration method. Similar particle sizes (155.6 ± 8.4 to 163.4 ± 11.3 nm) and polydispersity indices (0.42 ± 0.03 to 0.49 ± 0.04) were obtained when several non-ionic surfactants were tested to minimize particle agglomeration, with the smallest particles obtained with Triton X-100. When Triton X-100 was combined with a variety of ionic surfactants, smaller nanoparticles (97.1 ± 1.1 to 121.7 ± 5.7 nm) with a narrower particle size distribution (0.21 ± 0.001 to 0.25 ± 0.003) were produced. The SDS and Triton X-100 combination was chosen to evaluate other mcl-PHAs at 10 % (w/v) solids content. Slightly smaller nanoparticles were formed with carboxylated mcl-PHAs compared to mcl-PHAs having aliphatic pendant side chains. Mcl-PHA consisting of 18 % carboxylation (PHNC-2) formed a much smaller nanoparticles and higher zeta potential.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.

Characterization of Atrazine-Loaded Biodegradable Poly(Hydroxybutyrate-Co-Hydroxyvalerate) Microspheres

Polyhydroxybutyrate-co-hydroxyvalerate microspheres (PHBV-MS) were prepared as a delivery system for the herbicide atrazine (ATZ). Characterization of the system included investigation of in vitro release properties and genotoxicity. ATZ - PHBV-MS particle diameters showed a size distribution range of 1-13 mu m. Differential scanning calorimetry analyses indicated that ATZ was associated with the PHBV microparticles. The release profiles showed a different release behavior for the pure herbicide in solution, as compared with that containing ATZ-loaded PHBV-MS. Korsmeyer-Peppas model analyses showed that atrazine release from the microparticles occurred by a combination of diffusion through the matrix and partial diffusion through water-filled pores of the PHBV microparticles. A Lactuca sativa test result showed that the genotoxicity of ATZ-loaded PHBV-MP was decreased in relation to ATZ alone. The results demonstrate a viable biodegradable herbicide release system using atrazine for agrochemical purposes.

Encapsulation of Local Anesthetic Bupivacaine in Biodegradable Poly(DL-lactide-co-glycolide) Nanospheres: Factorial Design, Characterization and Cytotoxicity Studies

Local anesthetic agents cause temporary blockade of nerve impulses productiong insensitivity to painful stimuli in the area supplied by that nerve. Bupivacaine (BVC) is an amide-type local anesthetic widely used in surgery and obstetrics for sustained peripheral and central nerve blockade. in this study, we prepared and characterized nanosphere formulations containing BVC. To achieve these goals, BVC loaded poly(DL-lactide-co-glycolide) (PLGA) nanospheres (NS) were prepared by nanopreciptation and characterized with regard to size distribution, drug loading and cytotoxicity assays. The 2(3-1) factorial experimental design was used to study the influence of three different independent variables on nanoparticle drug loading. BVC was assayed by HPLC, the particle size and zeta potential were determined by dynamic light scattering. BVC was determined using a combined ultrafiltration-centrifugation technique. The results of optimized formulations showed a narrow size distribution with a polydispersivity of 0.05%, an average diameter of 236.7 +/- 2.6 nm and the zeta potential -2.93 +/- 1,10 mV. In toxicity studies with fibroblast 3T3 cells, BVC loaded-PLGA-NS increased cell viability, in comparison with the effect produced by free BVC. In this way, BVC-loaded PLGA-NS decreased BVC toxicity. The development of BVC formulations in carriers such as nanospheres could offer the possibility of controlling drug delivery in biological systems, prolonging the anesthetic effect and reducing toxicity.

Physicochemical Stability of Poly(lactide-co-glycolide) Nanocapsules Containing the Local Anesthetic Bupivacaine

This paper describes the preparation of poly(DL-lactide-co-glicolide) (PLGA) nanocapsules as a drug carrier system for the local anesthetic bupivacaine. The system was characterized and its stability investigated. The results showed a size distribution with a polydispersity index of 0.12, an average diameter of 148 nm, a zeta potential of -43.5 mV and an entrapment efficiency of 75.8%. The physicochemical properties of polymeric nanocapsule suspensions (average diameter, polydispersity, zeta potential and drug association efficiency) were evaluated as a function of time to determine the formulation stability. The formulation did not display major changes in these properties over the time, and it was considered stable up to 120 days of storage at room temperature. The results reported here which refer to the initial characterization of these new formulations for the local anesthetic bupivacaine show a promising potential for future in vivo studies.

Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two “mixed” predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.

Multifunctional Nanoparticles in Cancer: in vitro Characterization, in vivo Distribution

A novel biocompatible and biodegradable polymer, termed poly(Glycerol malate co-dodecanedioate) (PGMD), was prepared by thermal condensation method and used for fabrication of nanoparticles (NPs). PGMD NPs were prepared using the single oil emulsion technique and loaded with an imaging/hyperthermia agent (IR820) and a chemotherapeutic agent (doxorubicin, DOX). The size of the void PGMD NPs, IR820-PGMD NPs and DOX-IR820-PGMD NPs were approximately 90 nm, 110 nm, and 125 nm respectively. An acidic environment (pH=5.0) induced higher DOX and IR820 release compared to pH=7.4. DOX release was also enhanced by exposure to laser, which increased the temperature to 42°C. Cytotoxicity of DOX-IR820-PGMD NPs was comparable in MES-SA but was higher in Dx5 cells compared to free DOX plus IR820 (pIn vivomouse studies showed that NP formulation significantly improved the plasma half-life of IR820 after tail vein injection. Significant lower IR820 content was observed in kidney in DOX-IR820-PGMD NP treatment as compared to free IR820 treatment in our biodistribution studies (p

Medium-Chain-Length Poly (3-Hydroxyalkanoate) Nanoparticle Suspensions

Thesis (Master, Chemical Engineering) -- Queen's University, 2016-08-16 04:58:55.749

Distribution modelling of wild rabbit hunting yields in its original area (S Iberian Peninsula)

In this work we used the information of the Annual Hunting Reports (AHRs) to obtain a high-resolution model of the potential favourableness for wild rabbit harvesting in Andalusia (southern Spain), using environmental and land-use variables as predictors. We analysed 32,134 AHRs from the period 1993/2001 reported by 6049 game estates to estimate the average hunting yields of wild rabbit in each Andalusian municipality (n5771). We modelled the favourableness for obtaining good hunting yields using stepwise logistic regression on a set of climatic, orographical, land use, and vegetation variables. The favourability equation was used to create a downscaled image representing the favourableness of obtaining good hunting yields for the wild rabbit in 161 km squares in Andalusia, using the Idrisi Image Calculator. The variables that affected hunting yields of wild rabbit were altitude, dry wood crops (mainly olive groves, almond groves, and vineyards), temperature, pasture, slope, and annual number of frost days. The 161 km squares with high favourableness values are scattered throughout the territory, which seems to be caused mainly by the effect of vegetation. Finally, we obtained quality categories for the territory by combining the probability values given by logistic regression with those of the environmental favourability function.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

Modelling the distribution of Bonelli's eagle in Spain: Implications for conservation planning

Bonelli’s eagle, Hieraaetus fasciatus , has recently suffered a severe population decline and is currently endangered. Spain supports about 70% of the European population. We used stepwise logistic regression on a set of environmental, spatial and human variables to model Bonelli’s eagle distribution in the 5167 UTM 10 × 10 km quadrats of peninsular Spain. We obtained a model based on 16 variables, which allowed us to identify favourable and unfavourable areas for this species in Spain, as well as intermediate favourability areas. We assessed the stepwise progression of the model by comparing the model’s predictions in each step with those of the final model, and selected a parsimonious explanatory model based on three variables — slope, July temperature and precipitation — comprising 76% of the predictive capacity of the

Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula

We used the results of the Spanish Otter Survey of 1994–1996, a Geographic Information System and stepwise multiple logistic regression to model otter presence/absence data in the continental Spanish UTM 10 10-km squares. Geographic situation, indicators of human activity such as highways and major urban centers, and environmental variables related with productivity, water availability, altitude, and environmental energy were included in a logistic model that correctly classified about 73% of otter presences and absences. We extrapolated the model to the adjacent territory of Portugal, and increased the model’s spatial resolution by extrapolating it to 1 1-km squares in the whole Iberian Peninsula. The model turned out to be rather flexible, predicting, for instance, the species to be very restricted to the courses of rivers in some areas, and more widespread in others. This allowed us to determine areas where otter populations may be more vulnerable to habitat changes or harmful human interventions. # 2003 Elsevier Ltd. All rights reserved.

Analytics and Methods for Logistic System Design and Operations Management

Analytics is the technology working with the manipulation of data to produce information able to change the world we live every day. Analytics have been largely used within the last decade to cluster people’s behaviour to predict their preferences of items to buy, music to listen, movies to watch and even electoral preference. The most advanced companies succeded in controlling people’s behaviour using analytics. Despite the evidence of the super-power of analytics, they are rarely applied to the big data collected within supply chain systems (i.e. distribution network, storage systems and production plants). This PhD thesis explores the fourth research paradigm (i.e. the generation of knowledge from data) applied to supply chain system design and operations management. An ontology defining the entities and the metrics of supply chain systems is used to design data structures for data collection in supply chain systems. The consistency of this data is provided by mathematical demonstrations inspired by the factory physics theory. The availability, quantity and quality of the data within these data structures define different decision patterns. Ten decision patterns are identified, and validated on-field, to address ten different class of design and control problems in the field of supply chain systems research.

Effect Of Platform Connection And Abutment Material On Stress Distribution In Single Anterior Implant-supported Restorations: A Nonlinear 3-dimensional Finite Element Analysis.

Although various abutment connections and materials have recently been introduced, insufficient data exist regarding the effect of stress distribution on their mechanical performance. The purpose of this study was to investigate the effect of different abutment materials and platform connections on stress distribution in single anterior implant-supported restorations with the finite element method. Nine experimental groups were modeled from the combination of 3 platform connections (external hexagon, internal hexagon, and Morse tapered) and 3 abutment materials (titanium, zirconia, and hybrid) as follows: external hexagon-titanium, external hexagon-zirconia, external hexagon-hybrid, internal hexagon-titanium, internal hexagon-zirconia, internal hexagon-hybrid, Morse tapered-titanium, Morse tapered-zirconia, and Morse tapered-hybrid. Finite element models consisted of a 4×13-mm implant, anatomic abutment, and lithium disilicate central incisor crown cemented over the abutment. The 49 N occlusal loading was applied in 6 steps to simulate the incisal guidance. Equivalent von Mises stress (σvM) was used for both the qualitative and quantitative evaluation of the implant and abutment in all the groups and the maximum (σmax) and minimum (σmin) principal stresses for the numerical comparison of the zirconia parts. The highest abutment σvM occurred in the Morse-tapered groups and the lowest in the external hexagon-hybrid, internal hexagon-titanium, and internal hexagon-hybrid groups. The σmax and σmin values were lower in the hybrid groups than in the zirconia groups. The stress distribution concentrated in the abutment-implant interface in all the groups, regardless of the platform connection or abutment material. The platform connection influenced the stress on abutments more than the abutment material. The stress values for implants were similar among different platform connections, but greater stress concentrations were observed in internal connections.

The Inadequacy Of The Y-axis Of Growth (sngn) For The Vertical Pattern Assessment In Patients With Sagittal Discrepancies.

The aim of this cephalometric study was to evaluate the influence of the sagittal skeletal pattern on the 'Y-axis of growth' measurement in patients with different malocclusions. Lateral head films from 59 patients (mean age 16y 7m, ranging from 11 to 25 years) were selected after a subjective analysis of 1630 cases. Sample was grouped as follows: Group 1 - class I facial pattern; group 2 - class II facial pattern; and Group 3 - class III facial pattern. Two angular measurements, SNGoGn and SNGn, were taken in order to determine skeletal vertical facial pattern. A logistic regression with errors distributed according to a binomial distribution was used to test the influence of the sagittal relationship (Class I, II, III facial patterns) on vertical diagnostic measurement congruence (SNGoGn and SNGn). RESULTS show that the probability of congruence between the patterns SNGn and SNGoGn was relatively high (70%) for group 1, but for groups II (46%) and III (37%) this congruence was relatively low. The use of SNGn appears to be inappropriate to determine the vertical facial skeletal pattern of patients, due to Gn point shifting throughout sagittal discrepancies. Clinical Significance: Facial pattern determined by SNGn must be considered carefully, especially when severe sagittal discrepancies are present.