Subgeometric rates of convergence for a class of continuous-time Markov process

Let (Phi(t))(t is an element of R+) be a Harris ergodic continuous-time Markov process on a general state space, with invariant probability measure pi. We investigate the rates of convergence of the transition function P-t(x, (.)) to pi; specifically, we find conditions under which r(t) vertical bar vertical bar P-t (x, (.)) - pi vertical bar vertical bar -> 0 as t -> infinity, for suitable subgeometric rate functions r(t), where vertical bar vertical bar - vertical bar vertical bar denotes the usual total variation norm for a signed measure. We derive sufficient conditions for the convergence to hold, in terms of the existence of suitable points on which the first hitting time moments are bounded. In particular, for stochastically ordered Markov processes, explicit bounds on subgeometric rates of convergence are obtained. These results are illustrated in several examples.

Population differences in finger-length ratios: Ethnicity or latitude?

The relative length of the second and fourth fingers (the 2D:4D ratio) has been taken to be an indicator of prenatal exposure to testosterone, and hence possibly relevant to sexual orientation and other sex-differentiated behaviors. Studies have reported a difference in this ratio between Caucasian males in Britain and in the U.S.: higher average 2D:4D ratios were obtained in Britain. This raises the question of whether differences among different Caucasian gene pools were responsible or whether some environmental variable associated with latitude might be involved (e.g., exposure to sunlight or different day-length patterns). This question was explored by examining 2D:4D ratios for an Australian adolescent sample. The Australians were predominantly of British ancestry, but lived at distances from the equator more like those of the U.S. studies. The Australian 2D:4D ratios resembled those in Britain rather than those in the U.S., tending to exclude hypotheses related to latitude and making differences in gene pools a plausible explanation.

Effect of the average soft-segment length on the morphology and properties of segmented polyurethane nanocomposites

Two organically modified layered silicates (with small and large diameters) were incorporated into three segmented polyurethanes with various degrees of microphase separation. Microphase separation increased with the molecular weight of the poly(hexamethylene oxide) soft segment. The molecular weight of the soft segment did not influence the amount of polyurethane intercalating the interlayer spacing. Small-angle neutron scattering and differential scanning calorimetry data indicated that the layered silicates did not affect the microphase morphology of any host polymer, regardless of the particle diameter. The stiffness enhancement on filler addition increased as the microphase separation of the polyurethane decreased, presumably because a greater number of urethane linkages were available to interact with the filler. For comparison, the small nanofiller was introduced into a polyurethane with a poly(tetramethylene oxide) soft segment, and a significant increase in the tensile strength and a sharper upturn in the stress-strain curve resulted. No such improvement occurred in the host polymers with poly(hexamethylene oxide) soft segments. It is proposed that the nanocomposite containing the more hydrophilic and mobile poly(tetramethylene oxide) soft segment is capable of greater secondary bonding between the polyurethane chains and the organosilicate surface, resulting in improved stress transfer to the filler and reduced molecular slippage. (c) 2006 Wiley Periodicals, Inc.

The impact of supply chain performance measurement systems on dynamic behaviour in supply chains

The amplification of demand variation up a supply chain widely termed ‘the Bullwhip Effect’ is disruptive, costly and something that supply chain management generally seeks to minimise. Originally attributed to poor system design; deficiencies in policies, organisation structure and delays in material and information flow all lead to sub-optimal reorder point calculation. It has since been attributed to exogenous random factors such as: uncertainties in demand, supply and distribution lead time but these causes are not exclusive as academic and operational studies since have shown that orders and/or inventories can exhibit significant variability even if customer demand and lead time are deterministic. This increase in the range of possible causes of dynamic behaviour indicates that our understanding of the phenomenon is far from complete. One possible, yet previously unexplored, factor that may influence dynamic behaviour in supply chains is the application and operation of supply chain performance measures. Organisations monitoring and responding to their adopted key performance metrics will make operational changes and this action may influence the level of dynamics within the supply chain, possibly degrading the performance of the very system they were intended to measure. In order to explore this a plausible abstraction of the operational responses to the Supply Chain Council’s SCOR® (Supply Chain Operations Reference) model was incorporated into a classic Beer Game distribution representation, using the dynamic discrete event simulation software Simul8. During the simulation the five SCOR Supply Chain Performance Attributes: Reliability, Responsiveness, Flexibility, Cost and Utilisation were continuously monitored and compared to established targets. Operational adjustments to the; reorder point, transportation modes and production capacity (where appropriate) for three independent supply chain roles were made and the degree of dynamic behaviour in the Supply Chain measured, using the ratio of the standard deviation of upstream demand relative to the standard deviation of the downstream demand. Factors employed to build the detailed model include: variable retail demand, order transmission, transportation delays, production delays, capacity constraints demand multipliers and demand averaging periods. Five dimensions of supply chain performance were monitored independently in three autonomous supply chain roles and operational settings adjusted accordingly. Uniqueness of this research stems from the application of the five SCOR performance attributes with modelled operational responses in a dynamic discrete event simulation model. This project makes its primary contribution to knowledge by measuring the impact, on supply chain dynamics, of applying a representative performance measurement system.

An accurate Markov model for slotted CSMA/CA algorithm in IEEE 802.15.4 networks

In this letter we propose an Markov model for slotted CSMA/CA algorithm working in a non-acknowledgement mode, specified in IEEE 802.15.4 standard. Both saturation throughput and energy consumption are modeled as functions of backoff window size, number of contending devices and frame length. Simulations show that the proposed model can achieve a very high accuracy (less than 1% mismatch) if compared to all existing models (bigger than 10% mismatch).

The upstream Variable Number Tandem Repeat polymorphism of the monoamine oxidase type A gene influences trigeminal pain-related evoked responses

Monoamines have an important role in neural plasticity, a key factor in cortical pain processing that promotes changes in neuronal network connectivity. Monoamine oxidase type A (MAOA) is an enzyme that, due to its modulating role in monoaminergic activity, could play a role in cortical pain processing. The X-linked MAOA gene is characterized by an allelic variant of length, the MAOA upstream Variable Number Tandem Repeat (MAOA-uVNTR) region polymorphism. Two allelic variants of this gene are known, the high-activity MAOA (HAM) and low-activity MAOA (LAM). We investigated the role of MAOA-uVNTR in cortical pain processing in a group of healthy individuals measured by the trigeminal electric pain-related evoked potential (tPREP) elicited by repeated painful stimulation. A group of healthy volunteers was genotyped to detect MAOA-uVNTR polymorphism. Electrical tPREPs were recorded by stimulating the right supraorbital nerve with a concentric electrode. The N2 and P2 component amplitude and latency as well as the N2-P2 inter-peak amplitude were measured. The recording was divided into three blocks, each containing 10 consecutive stimuli and the N2-P2 amplitude was compared between blocks. Of the 67 volunteers, 37 were HAM and 30 were LAM. HAM subjects differed from LAM subjects in terms of amplitude of the grand-averaged and first-block N2-P2 responses (HAM>LAM). The N2-P2 amplitude decreased between the first and third block in HAM subjects but not LAM subjects. The MAOA-uVNTR polymorphism seemed to influence the brain response in a repeated tPREP paradigm and suggested a role of the MAOA as a modulator of neural plasticity related to cortical pain processing. Monoamines have an important role in neural plasticity, a key factor in cortical pain processing that promotes changes in neuronal network connectivity. Monoamine oxidase type A (MAOA) is an enzyme that, due to its modulating role in monoaminergic activity, could play a role in cortical pain processing. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Combination of 16S rRNA variable regions provides a detailed analysis of bacterial community dynamics in the lungs of cystic fibrosis patients

Chronic bronchopulmonary bacterial infections remain the most common cause of morbidity and mortality among patients with cystic fibrosis (CF). Recent community sequencing work has now shown that the bacterial community in the CF lung is polymicrobial. Identifying bacteria in the CF lung through sequencing can be costly and is not practical for many laboratories. Molecular techniques such as terminal restriction fragment length polymorphism or amplicon length heterogeneity-polymerase chain reaction (LH-PCR) can provide many laboratories with the ability to study CF bacterial communities without costly sequencing. The aim of this study was to determine if the use of LH-PCR with multiple hypervariable regions of the 16S rRNA gene could be used to identify organisms found in sputum DNA. This work also determined if LH-PCR could be used to observe the dynamics of lung infections over a period of time. Nineteen samples were analysed with the V1 and the V1_V2 region of the 16S rRNA gene. Based on the amplicon size present in the V1_V2 region, Pseudomonas aeruginosa was confirmed to be in all 19 samples obtained from the patients. The V1 region provided a higher power of discrimination between bacterial profiles of patients. Both regions were able to identify trends in the bacterial population over a period of time. LH profiles showed that the CF lung community is dynamic and that changes in the community may in part be driven by the patient's antibiotic treatment. LH-PCR is a tool that is well suited for studying bacterial communities and their dynamics.

The relationship of aptitude, course grades, and program length, to performance on a standards based test (NCLEX -RN)

The purpose of this study was threefold: first, to investigate variables associated with learning, and performance as measured by the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The second purpose was to validate the predictive value of the Assessment Technologies Institute (ATI) achievement exit exam, and lastly, to provide a model that could be used to predict performance on the NCLEX-RN, with implications for admission and curriculum development. The study was based on school learning theory, which implies that acquisition in school learning is a function of aptitude (pre-admission measures), opportunity to learn, and quality of instruction (program measures). Data utilized were from 298 graduates of an associate degree nursing program in the Southeastern United States. Of the 298 graduates, 142 were Hispanic, 87 were Black, non-Hispanic, 54 White, non-Hispanic, and 15 reported as Others. The graduates took the NCLEX-RN for the first time during the years 2003–2005. This study was a predictive, correlational design that relied upon retrospective data. Point biserial correlations, and chi-square analyses were used to investigate relationships between 19 selected predictor variables and the dichotomous criterion variable, NCLEX-RN. The correlation and chi square findings indicated that men did better on the NCLEX-RN than women; Blacks had the highest failure rates, followed by Hispanics; older students were more likely to pass the exam than younger students; and students who passed the exam started and completed the nursing program with a higher grade point average, than those who failed the exam. Using logistic regression, five statistical models that used variables associated with learning and student performance on the NCLEX-RN were tested with a model adapted from Bloom's (1976) and Carroll's (1963) school learning theories. The derived model included: NCLEX-RNsuccess = f (Nurse Entrance Test and advanced medical-surgical nursing course grade achieved). The model demonstrates that student performance on the NCLEX-RN can be predicted by one pre-admission measure, and a program measure. The Assessment Technologies Institute achievement exit exam (an outcome measure) had no predictive value for student performance on the NCLEX-RN. The model developed accurately predicted 94% of the student's successful performance on the NCLEX-RN.

Toy model to describe the effect of positional blocklike disorder in metamaterials composites

We study theoretically the effect of a new type of blocklike positional disorder on the effective electromagnetic properties of one-dimensional chains of resonant, high-permittivity dielectric particles, where particles are arranged into perfectly well-ordered blocks whose relative position is a random variable. This creates a finite order correlation length that mimics the situation encountered in metamaterials fabricated through self-assembled techniques, whose structures often display short-range order between near neighbors but long-range disorder, due to stacking defects. Using a spectral theory approach combined with a principal component statistical analysis, we study, in the long-wavelength regime, the evolution of the electromagnetic response when the composite filling fraction and the block size are changed. Modifications in key features of the resonant response (amplitude, width, etc.) are investigated, showing a regime transition for a filling fraction around 50%.

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.

Bayesian nonparametric latent variable models

L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’expliquer le processus génératif des données. Il existe diverses approches permettant d’identifier le nombre approprié de variables cachées d’un modèle. Cette thèse s’intéresse aux méthodes Bayésiennes nonparamétriques permettant de déterminer le nombre de variables cachées à utiliser ainsi que leur dimensionnalité. La popularisation des statistiques Bayésiennes nonparamétriques au sein de la communauté de l’apprentissage automatique est assez récente. Leur principal attrait vient du fait qu’elles offrent des modèles hautement flexibles et dont la complexité s’ajuste proportionnellement à la quantité de données disponibles. Au cours des dernières années, la recherche sur les méthodes d’apprentissage Bayésiennes nonparamétriques a porté sur trois aspects principaux : la construction de nouveaux modèles, le développement d’algorithmes d’inférence et les applications. Cette thèse présente nos contributions à ces trois sujets de recherches dans le contexte d’apprentissage de modèles à variables cachées. Dans un premier temps, nous introduisons le Pitman-Yor process mixture of Gaussians, un modèle permettant l’apprentissage de mélanges infinis de Gaussiennes. Nous présentons aussi un algorithme d’inférence permettant de découvrir les composantes cachées du modèle que nous évaluons sur deux applications concrètes de robotique. Nos résultats démontrent que l’approche proposée surpasse en performance et en flexibilité les approches classiques d’apprentissage. Dans un deuxième temps, nous proposons l’extended cascading Indian buffet process, un modèle servant de distribution de probabilité a priori sur l’espace des graphes dirigés acycliques. Dans le contexte de réseaux Bayésien, ce prior permet d’identifier à la fois la présence de variables cachées et la structure du réseau parmi celles-ci. Un algorithme d’inférence Monte Carlo par chaîne de Markov est utilisé pour l’évaluation sur des problèmes d’identification de structures et d’estimation de densités. Dans un dernier temps, nous proposons le Indian chefs process, un modèle plus général que l’extended cascading Indian buffet process servant à l’apprentissage de graphes et d’ordres. L’avantage du nouveau modèle est qu’il admet les connections entres les variables observables et qu’il prend en compte l’ordre des variables. Nous présentons un algorithme d’inférence Monte Carlo par chaîne de Markov avec saut réversible permettant l’apprentissage conjoint de graphes et d’ordres. L’évaluation est faite sur des problèmes d’estimations de densité et de test d’indépendance. Ce modèle est le premier modèle Bayésien nonparamétrique permettant d’apprendre des réseaux Bayésiens disposant d’une structure complètement arbitraire.

Development of variable displacement oil pump for internal combustion engines

In the last years the need to develop more environmentally friendly and efficient cars as led to the development of several technologies to improve the performance of internal combustion engines, a large part of the innovations are focused in the auxiliary systems of the engine, including, the oil pump, this is an element of great importance in the dynamics of the engine as well a considerable energy consumer. Most solutions for oil pumps to this day are fixed displacement, for medium and high speeds, the pump flow rate is higher than the needs of the engine, this excess flow leads to the need for recirculation of the fluid which represents a waste of energy. Recently, technological advances in this area have led to the creation of variable displacement oil pumps, these have become a 'must have' due to the numerous advantages they bring, although the working principle of vane or piston pumps is relatively well known, the application of this technology for the automotive industry is new and brings new challenges. The focus of this dissertation is to develop a new concept of variable displacement system for automotive oil pumps. The main objective is to obtain a concept that is totally adaptable to existing solutions on the market (engines), both dimensionally as in performance specifications, having at the same time an innovative mechanical system for obtaining variable displacement. The developed design is a vane pump with variable displacement going in line with existing commercial solutions, however, the variation of the eccentricity commonly used to provide an variable displacement delivery is not used, the variable displacement is achieved without varying the eccentricity of the system but with a variation of the length of the pumping chamber. The principle of operation of the pump is different to existing solutions while maintaining the ability to integrate standard parts such as control valves and mechanical safety valves, the pump is compatible with commercial solutions in terms of interfaces for connection between engine systems and pump. A concept prototype of the product was obtained in order to better evaluate the validity of the concept. The developed concept represents an innovation in oil pumps design, being unique in its mechanical system for variable displacement delivery.

An investigation into the folding and assembly of engineered antibodies and novel antibody formats

Monoclonal antibodies and novel antibody formats are currently one of the principal therapeutic in the biopharmaceutical industry worldwide and are widely used in the treatment of autoimmune diseases and cancer. It is for this reason that the productivity and quality of antibody production requires improvement; specifically investigations into the engineering of antibodies and any issues that may arise from the production of these therapeutics. The work presented in this thesis describes an investigation into the folding and assembly of seven antibodies plus the novel antibody format FabFv. IgG is comprised of two identical HCs and two identical LCs. The folding process of immunoglobulin is controlled by the CH1 domain within the HC. The CH1 domain remains in a disordered state and is sequestered by BiP in the endoplasmic reticulum. Upon the addition of a folded CL domain, BiP is displaced, the CH1 domain is able to fold and the complete IgG protein can then be secreted from the cell. The results presented in this thesis however, have outlined an additional mechanism for the folding of the CH1 domain. We have shown that the CH1 domain is able to fold in the absence of LC resulting in the secretion of HC dimers in a VH dependent manner. The proposed mechanism for the secretion of HC dimers suggests that some VH domains can interact with each other in order to bring the CH1 domains in close proximity to enable folding to occur. As HC dimer secretion is a hindrance in antibody production, this result has highlighted an engineering target to improve antibody yield. Examination of the folding of IgG4 with the variable region A33 has revealed the inability to secrete LC dimers, cleavage of the HC during expression and secretion of HC dimers in the Fab, FabFv and full length forms. The attributes described have also been shown to be variable region dependent. This has introduced a new concept that the variable domain is important in determining the expression and secretion of antibodies and their individual chains. Pulse chase and 2D gel electrophoresis analysis of the novel antibody format FabFv has revealed that the folding and expression of the LC and HC causes multimeric species of FabFv to be secreted, as opposed to the monomeric form which is the desired therapeutic. Our hypothesis is that this process occurs via a LC dependent mechanism. The proposed hypothesis suggests that further engineering to the LC could diminish the formation and secretion of FabFv multimers. The results from these investigations can be applied to increase the productivity of therapeutics and increase the biological understanding of the domain interactions of IgG during folding, assembly and secretion.

CHARACTERIZATION OF POLYUBIQUITIN CHAINS BY MASS SPECTROMETRY

The work outlined in this dissertation will allow biochemists and cellular biologists to characterize polyubiquitin chains involved in their cellular environment by following a facile mass spectrometric based workflow. The characterization of polyubiquitin chains has been of interest since their discovery in 1984. The profound effects of ubiquitination on the movement and processing of cellular proteins depend exclusively on the structures of mono and polyubiquitin modifications anchored or unanchored on the protein within the cellular environment. However, structure-function studies have been hindered by the difficulty in identifying complex chain structures due to limited instrument capabilities of the past. Genetic mutations or reiterative immunoprecipitations have been used previously to characterize the polyubiquitin chains, but their tedium makes it difficult to study a broad ubiquitinome. Top-down and middle-out mass spectral based proteomic studies have been reported for polyubiquitin and have had success in characterizing parts of the chain, but no method to date has been successful at differentiating all theoretical ubiquitin chain isomers (ubiquitin chain lengths from dimer to tetramer alone have 1340 possible isomers). The workflow presented here can identify chain length, topology and linkages present using a chromatographic-time-scale compatible, LC-MS/MS based workflow. To accomplish this feat, the strategy had to exploit the most recent advances in top-down mass spectrometry. This included the most advanced electron transfer dissociation (ETD) activation and sensitivity for large masses from the orbitrap Fusion Lumos. The spectral interpretation had to be done manually with the aid of a graphical interface to assign mass shifts because of a lack of software capable to interpret fragmentation across isopeptide linkages. However, the method outlined can be applied to any mass spectral based system granted it results in extensive fragmentation across the polyubiquitin chain; making this method adaptable to future advances in the field.