Sequential slotted amplify-and-forward with partial relay isolation

In this paper, we propose a novel relay ordering and scheduling strategy for the sequential slotted amplify-and-forward (SAF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are grouped into two relay clusters based on their respective locations. The proposed strategy achieves partial relay isolation and decreases the decoding complexity at the destination. We show that the DMT upper bound of sequential-SAF with the proposed strategy outperforms other amplify and forward protocols and is more practical compared to the relay isolation assumption made in the original paper [1]. Simulation result shows that the sequential-SAF protocol with the proposed strategy has better outage performance compared to the existing AF and non-cooperative protocols in high SNR regime.

Sequential slotted amplify-decode-and-forward

In this paper, we propose a novel slotted hybrid cooperative protocol named the sequential slotted amplify-decodeand-forward (SADF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are divided into two different groups and each relay either amplifies or decodes the received signal. We first compute the optimal DMT of the proposed protocol with the assumption of perfect decoding at the DF relays. We then derive the DMT closed-form expression of the proposed sequential-SADF and obtain the proximity gain bound for achieving the optimal DMT. With the proximity gain bound, we then found the distance ratio to achieve the optimal DMT performance. Simulation result shows that the proposed protocol with high proximity gain outperforms other cooperative communication protocols in high SNR regime.

Using patterns co-occurrence matrix for cleaning closed sequential patterns for text mining

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

An influenza virus inspired polymer system for the timed release of siRNA

Small interfering RNA silences specific genes by interfering with mRNA translation, and acts to modulate or inhibit specific biological pathways; a therapy that holds great promise in the cure of many diseases. However, the naked small interfering RNA is susceptible to degradation by plasma and tissue nucleases and due to its negative charge unable to cross the cell membrane. Here we report a new polymer carrier designed to mimic the influenza virus escape mechanism from the endosome, followed by a timed release of the small interfering RNA in the cytosol through a self-catalyzed polymer degradation process. Our polymer changes to a negatively charged and non-toxic polymer after the release of small interfering RNA, presenting potential for multiple repeat doses and long-term treatment of diseases.

Mathematical modelling of controlled drug release from polymer micro-spheres : incorporating the effects of swelling, diffusion and dissolution via moving boundary problems

Controlled drug delivery is a key topic in modern pharmacotherapy, where controlled drug delivery devices are required to prolong the period of release, maintain a constant release rate, or release the drug with a predetermined release profile. In the pharmaceutical industry, the development process of a controlled drug delivery device may be facilitated enormously by the mathematical modelling of drug release mechanisms, directly decreasing the number of necessary experiments. Such mathematical modelling is difficult because several mechanisms are involved during the drug release process. The main drug release mechanisms of a controlled release device are based on the device’s physiochemical properties, and include diffusion, swelling and erosion. In this thesis, four controlled drug delivery models are investigated. These four models selectively involve the solvent penetration into the polymeric device, the swelling of the polymer, the polymer erosion and the drug diffusion out of the device but all share two common key features. The first is that the solvent penetration into the polymer causes the transition of the polymer from a glassy state into a rubbery state. The interface between the two states of the polymer is modelled as a moving boundary and the speed of this interface is governed by a kinetic law. The second feature is that drug diffusion only happens in the rubbery region of the polymer, with a nonlinear diffusion coefficient which is dependent on the concentration of solvent. These models are analysed by using both formal asymptotics and numerical computation, where front-fixing methods and the method of lines with finite difference approximations are used to solve these models numerically. This numerical scheme is conservative, accurate and easily implemented to the moving boundary problems and is thoroughly explained in Section 3.2. From the small time asymptotic analysis in Sections 5.3.1, 6.3.1 and 7.2.1, these models exhibit the non-Fickian behaviour referred to as Case II diffusion, and an initial constant rate of drug release which is appealing to the pharmaceutical industry because this indicates zeroorder release. The numerical results of the models qualitatively confirms the experimental behaviour identified in the literature. The knowledge obtained from investigating these models can help to develop more complex multi-layered drug delivery devices in order to achieve sophisticated drug release profiles. A multi-layer matrix tablet, which consists of a number of polymer layers designed to provide sustainable and constant drug release or bimodal drug release, is also discussed in this research. The moving boundary problem describing the solvent penetration into the polymer also arises in melting and freezing problems which have been modelled as the classical onephase Stefan problem. The classical one-phase Stefan problem has unrealistic singularities existed in the problem at the complete melting time. Hence we investigate the effect of including the kinetic undercooling to the melting problem and this problem is called the one-phase Stefan problem with kinetic undercooling. Interestingly we discover the unrealistic singularities existed in the classical one-phase Stefan problem at the complete melting time are regularised and also find out the small time behaviour of the one-phase Stefan problem with kinetic undercooling is different to the classical one-phase Stefan problem from the small time asymptotic analysis in Section 3.3. In the case of melting very small particles, it is known that surface tension effects are important. The effect of including the surface tension to the melting problem for nanoparticles (no kinetic undercooling) has been investigated in the past, however the one-phase Stefan problem with surface tension exhibits finite-time blow-up. Therefore we investigate the effect of including both the surface tension and kinetic undercooling to the melting problem for nanoparticles and find out the the solution continues to exist until complete melting. The investigation of including kinetic undercooling and surface tension to the melting problems reveals more insight into the regularisations of unphysical singularities in the classical one-phase Stefan problem. This investigation gives a better understanding of melting a particle, and contributes to the current body of knowledge related to melting and freezing due to heat conduction.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

Polymer nanocarrier system for endosome escape and timed release of siRNA with complete gene silencing and cell death in cancer cells

An influenza virus-inspired polymer mimic nanocarrier was used to deliver siRNA for specific and near complete gene knockdown of an osteoscarcom cell line (U-2SO). The polymer was synthesized by single-electron transfer living radical polymerization (SET-LRP) at room temperature to avoid complexities of transfer to monomer or polymer. It was the only LRP method that allowed good block copolymer formation with a narrow molecular weight distribution. At nitrogen to phosphorus (N/P) ratios of equal to or greater than 20 (greater than a polymer concentration of 13.8 μg/mL) with polo-like kinase 1 (PLK1) siRNA gave specific and near complete (>98%) cell death. The polymer further degrades to a benign polymer that showed no toxicity even at polymer concentrations of 200 μg/mL (or N/P ratio of 300), suggesting that our polymer nanocarrier can be used as a very effective siRNA delivery system and in a multiple dose administration. This work demonstrates that with a well-designed delivery device, siRNA can specifically kill cells without the inclusion of an additional clinically used highly toxic cochemotherapeutic agent. Our work also showed that this excellent delivery is sensitive for the study of off-target knockdown of siRNA.

Modulation of in vitro platelet 5-HT release by species of Erythrina and Cymbopogon

Extracts of Australian plants were screened to detect constituents affecting adenosine di-phosphate (ADP) induced platelet aggregation and [14C]5-hydroxytryptamine (5-HT) release. Extracts of four tested plants including, Eremophila gilesii, Erythrina vespertilio, Cymbopogon ambiguus, and Santalum acuminatum, were found to cause significant inhibition of platelet 5-HT release. Inhibition levels ranged from 56-98%, and was not due to the non-specific effects of protein binding tannins. These extracts, and those we have previously identified as being active, were examined further to determine if they affect epinephrine (EPN), arachidonic acid (A.A) or collagen stimulated platelet aggregation and 5-HT release. Among those extracts investigated, we found that both the methanolic extract of E. vespertilio and the dichloromethane (DCM) extract of C. ambiguus were most potent and caused significant inhibition of platelet activation induced by EPN, A.A and to a lesser extent by collagen. Inhibition of ADP induced platelet 5-HT release by both of these extracts, was dose-dependent, with IC50 values for E. vespertilio and C. ambiguus estimated to be 20.4 microl (1.855 mg/ml) and 8.34 microl (0.758 mg/ml), respectively. Overall, C. ambiguus exhibited most activity and also caused dose-dependent inhibition of A.A induced platelet activation. These results indicate that inhibition may occur specifically at a site within the A.A pathway, and suggest the presence of a cyclo-oxygenase inhibitor. Both E. vespertilio and C. ambiguus are reported to be traditional headache treatments, with the present study providing evidence that they affect 5-HT release.

Inhibition of platelet aggregation and 5-HT release by extracts of Australian plants used traditionally as headache treatments

To identify potential migraine therapeutics, extracts of eighteen plants were screened to detect plant constituents affecting ADP induced platelet aggregation and [14C]5-hydroxytryptamine (5-HT) release. Extracts of the seven plants exhibiting significant inhibition of platelet function were reanalysed in the presence of polyvinyl pyrrolidone (PVP) to remove polyphenolic tannins that precipitate proteins. Two of these extracts no longer exhibited inhibition of platelet activity after removal of tannins. However, extracts of Crataegus monogyna, Ipomoea pes-caprae, Eremophila freelingii, Eremophila longifolia, and Asteromyrtus symphyocarpa still potently inhibited ADP induced human platelet [14C]5-HT release in vitro, with levels ranging from 62 to 95% inhibition. I. pes-caprae, and C. monogyna also caused significant inhibition of ADP induced platelet aggregation. All of these plants have been previously used as traditional headache treatments, except for C. monogyna which is used primarily for protective effects on the cardiovascular system. Further studies elucidating the compounds that are responsible for these anti-platelet effects are needed to determine their exact mechanism of action.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

Quantification of ebullitive and diffusive methane release to atmosphere from a water storage

Accurately quantifying total freshwater storage methane release to atmosphere requires the spatial–temporal measurement of both diffusive and ebullitive emissions. Existing floating chamber techniques provide localised assessment of methane flux, however, significant errors can arise when weighting and extrapolation to the entire storage, particularly when ebullition is significant. An improved technique has been developed that compliments traditional chamber based experiments to quantify the storage-scale release of methane gas to atmosphere through ebullition using the measurements from an Optical Methane Detector (OMD) and a robotic boat. This provides a conservative estimate of the methane emission rate from ebullition along with the bubble volume distribution. It also georeferences the area of ebullition activity across entire storages at short temporal scales. An assessment on Little Nerang Dam in Queensland, Australia, demonstrated whole storage methane release significantly differed spatially and throughout the day. Total methane emission estimates showed a potential 32-fold variation in whole-of-dam rates depending on the measurement and extrapolation method and time of day used. The combined chamber and OMD technique showed that 1.8–7.0% of the surface area of Little Nerang Dam is accounting for up to 97% of total methane release to atmosphere throughout the day. Additionally, over 95% of detectable ebullition occurred in depths less than 12 m during the day and 6 m at night. This difference in spatial and temporal methane release rate distribution highlights the need to monitor significant regions of, if not the entire, water storage in order to provide an accurate estimate of ebullition rates and their contribution to annual methane emissions.

Exenatide extended-release; clinical trials, patient preference, and economic considerations

Type 2 diabetes remains an escalating world-wide problem, despite a range of treatments. The revelation that insulin secretion is under the control of a gut hormone, glucagon-like peptide 1 (GLP-1) led to a new paradigm in the management of type 2 diabetes, medicines that directly stimulate, or that prolong the actions of the endogenous GLP-1, at its receptors. Exenatide is an agonist at the GLP-1 receptors, and was initially developed as a subcutaneous twice daily medication, ExBID. The clinical trials with ExBID established a role for exenatide in the treatment of type 2 diabetes. Subsequently, once weekly exenatide (ExQW) was shown to have advantages over ExBID, and there is now more emphasis on the development of ExQW. ExQW alone reduces glycosylated haemoglobin (HbA1c) and body weight, and is well tolerated. ExQW has been compared to sitagliptin, pioglitazone and metformin, and shown to have a greater ability to reduce HbA1c than these other medicines. The only preparation of insulin, which ExQW has been compared to, is insulin glargine, and the ExQW has some favourable properties in this comparison, notably causing weight loss, compared to the gain with insulin glargine. ExQW has been compared to another GLP-1 receptor agonist, liraglutide, and ExQW is non-inferior to liraglutide in reducing HbA1c. The small amount of evidence available, shows that subjects with type 2 diabetes, prefer ExQW to ExBID, and that adherence was high to these in the clinical trial setting. Healthcare and economic modelling suggests that ExQW will reduce diabetic complications and be cost-effective, compared to other medications, with long term use. Little is known about whether subjects with type 2 diabetes prefer ExQW to other medicines, and whether adherence is good to ExQW in practice, and these important topics require further study.

Effect of chemical conjugation of L-leucine to chitosan on the dispersibility and drug release of a dry powder inhaler nanoparticle formulation

Purpose: This study investigated the effect of chemical conjugation of the amino acid L-leucine to the polysaccharide chitosan on the dispersibility and drug release pattern of a polymeric nanoparticle (NP)-based controlled release dry powder inhaler (DPI) formulation. Methods: A chemical conjugate of L-leucine with chitosan was synthesized and characterized by Infrared (IR) Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy, Elemental Analysis and X-ray Photoelectron Spectroscopy (XPS). Nanoparticles of both chitosan and its conjugate were prepared by a water-in-oil emulsification – glutaraldehyde cross-linking method using the antihypertensive agent, diltiazem (Dz) hydrochloride as the model drug. The surface morphology and particle size distribution of the nanoparticles were determined by Scanning Electron Microscopy (SEM) and Dynamic Light Scattering (DLS). The dispersibility of the nanoparticle formulation was analysed by a Twin Stage Impinger (TSI) with a Rotahaler as the DPI device. Deposition of the particles in the different stages was determined by gravimetry and the amount of drug released was analysed by UV spectrophotometry. The release profile of the drug was studied in phosphate buffered saline at 37 ⁰C and analyzed by UV spectrophotometry. Results: The TSI study revealed that the fine particle fractions (FPF), as determined gravimetrically, for empty and drug-loaded conjugate nanoparticles were significantly higher than for the corresponding chitosan nanoparticles (24±1.2% and 21±0.7% vs 19±1.2% and 15±1.5% respectively; n=3, p<0.05). The FPF of drug-loaded chitosan and conjugate nanoparticles, in terms of the amount of drug determined spectrophotometrically, had similar values (21±0.7% vs 16±1.6%). After an initial burst, both chitosan and conjugate nanoparticles showed controlled release that lasted about 8 to 10 days, but conjugate nanoparticles showed twice as much total drug release compared to chitosan nanoparticles (~50% vs ~25%). Conjugate nanoparticles also showed significantly higher dug loading and entrapment efficiency than chitosan nanoparticles (conjugate: 20±1% & 46±1%, chitosan: 16±1% & 38±1%, n=3, p<0.05). Conclusion: Although L-leucine conjugation to chitosan increased dispersibility of formulated nanoparticles, the FPF values are still far from optimum. The particles showed a high level of initial burst release (chitosan, 16% and conjugate, 31%) that also will need further optimization.

Detecting news topics from microblogs using sequential pattern mining

This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.