Auxin dynamics after decapitation are not correlated with the initial growth of axillary buds

One of the first and most enduring roles identified for the plant hormone auxin is the mediation of apical dominance. Many reports have claimed that reduced stem indole-3-acetic acid (IAA) levels and/ or reduced basipetal IAA transport directly or indirectly initiate bud growth in decapitated plants. We have tested whether auxin inhibits the initial stage of bud release, or subsequent stages, in garden pea (Pisum sativum) by providing a rigorous examination of the dynamics of auxin level, auxin transport, and axillary bud growth. We demonstrate that after decapitation, initial bud growth occurs prior to changes in IAA level or transport in surrounding stem tissue and is not prevented by an acropetal supply of exogenous auxin. We also show that auxin transport inhibitors cause a similar auxin depletion as decapitation, but do not stimulate bud growth within our experimental time- frame. These results indicate that decapitation may trigger initial bud growth via an auxin-independent mechanism. We propose that auxin operates after this initial stage, mediating apical dominance via autoregulation of buds that are already in transition toward sustained growth.

Determinants in the development of advanced nursing practice: a case study of primary-care settings in Hong Kong

Different factors have been shown to influence the development of models of advanced nursing practice (ANP) in primary-care settings. Although ANP is being developed in hospitals in Hong Kong, China, it remains undeveloped in primary care and little is known about the factors determining the development of such a model. The aims of the present study were to investigate the contribution of different models of nursing practice to the care provided in primary-care settings in Hong Kong, and to examine the determinants influencing the development of a model of ANP in such settings. A multiple case study design was selected using both qualitative and quantitative methods of data collection. Sampling methods reflected the population groups and stage of the case study. Sampling included a total population of 41 nurses from whom a secondary volunteer sample was drawn for face-to-face interviews. In each case study, a convenience sample of 70 patients were recruited, from whom 10 were selected purposively for a semi-structured telephone interview. An opportunistic sample of healthcare professionals was also selected. The within-case and cross-case analysis demonstrated four major determinants influencing the development of ANP: (1) current models of nursing practice; (2) the use of skills mix; (3) the perceived contribution of ANP to patient care; and (4) patients' expectations of care. The level of autonomy of individual nurses was considered particularly important. These determinants were used to develop a model of ANP for a primary-care setting. In conclusion, although the findings highlight the complexity determining the development and implementation of ANP in primary care, the proposed model suggests that definitions of advanced practice are appropriate to a range of practice models and cultural settings. However, the findings highlight the importance of assessing the effectiveness of such models in terms of cost and long-term patient outcomes.

Analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Analytical Approximations to Predict Performance Measures of Manufacturing Systems with Job Failures and Parallel Processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Immunotherapy for the treatment of poor prognosis cancers

Cancer is amongst the leading causes of death worldwide and the number one cause in the developed world. Every year there are close to 10 million cancer related deaths and this corresponds to hundreds of millions of euro in health care costs and lost productivity, placing a substantial drain on the economy. The efficacy of traditional treatment modalities for cancer therapy, such as surgery, radiotherapy and chemotherapy has plateaued, and while they are undoubtedly effective at prolonging patient lifespan, there is a high rate of adverse side effects and fatal reoccurrence. Currently, there is a huge amount of interest in the areas of cancer immunosurveillance and cancer immuno-editing, which explain some of the complex interactions between the host immune system and cancer. If left unchecked, cancerous malignancies have the ability to generate an immunosuppressive microenvironment, effectively shielding themselves from elimination and promoting tumour growth and progression. To overcome this, the potential of the immune system must be harnessed and the work undertaken in this thesis sought to contribute to this goal. Focus was placed on using novel therapies, combining tumour ablation with immune-modulating antibodies to maximise tumour elimination in an immune dependent manner, to overcome immunosuppression and promote immune activation. Chapter 2 focuses on the use of ECT as a method of tumour ablation and its effects on the immune system. ECT proved to be effective at inhibiting the tumour growth both in vitro and in vivo, and conferred significant survival advantages in both small and large animal models. More importantly, ECT proved to cause tumour death in an immune dependent manner, displaying the hallmarks of Immunogenic Cell Death, increases in immune cell infiltration and generating tumour-specific immune responses. Chapter 3 focuses on combining ECT with immune checkpoint blockade inhibitors; anti- CTLA-4 and anti-PD-1. Both combinations proved to be effective at inhibiting both primary and distal tumour growth, indicating the generation of tumour specific immune responses and prolonged animal survival. In addition, the treatments caused increases in the levels of certain intra-tumoural immune cell subsets and modulated the cytokine profile of treated animals in a way that was favourable overall. Chapter 4 focuses on the combining ECT with an anti-iCOS agonist antibody, capable of causing immune co-stimulation. This novel combinational therapy proved to be the most effective by far, with a high cure rate achieved across a number of different in vivo tumour models. Total regression was seen in both primary and distal tumours, as well as spontaneous metastases, with the tumour specific immune response generated conferring total protection to animals on tumour rechallenge. Overall the data presented here adds further insight into the area of cancer immunotherapy with some of the novel combinational therapies demonstrating substantial clinic potential.

Linear-wavelet models for non-linear identification applied to a pressure plant

A nonlinear regression structure comprising a wavelet network and a linear term is proposed for system identification. The theoretical foundation of the approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such models is described and the approach is tested with experimental data.

System identification and control of an aerobot drive system

Fast thrust changes are important for authoritive control of VTOL micro air vehicles. Fixed-pitch rotors that alter thrust by varying rotor speed require high-bandwidth control systems to provide adequate performace. We develop a feedback compensator for a brushless hobby motor driving a custom rotor suitable for UAVs. The system plant is identified using step excitation experiments. The aerodynamic operating conditions of these rotors are unusual and so experiments are performed to characterise expected load disturbances. The plant and load models lead to a proportional controller design capable of significantly decreasing rise-time and propagation of disturbances, subject to bus voltage constraints.

Identification of factors contributing to recurrence of venous leg ulcers

Background and Significance Venous leg ulcers are a significant cause of chronic ill-health for 1–3% of those aged over 60 years, increasing in incidence with age. The condition is difficult and costly to heal, consuming 1–2.5% of total health budgets in developed countries and up to 50% of community nursing time. Unfortunately after healing, there is a recurrence rate of 60 to 70%, frequently within the first 12 months after heaing. Although some risk factors associated with higher recurrence rates have been identified (e.g. prolonged ulcer duration, deep vein thrombosis), in general there is limited evidence on treatments to effectively prevent recurrence. Patients are generally advised to undertake activities which aim to improve the impaired venous return (e.g. compression therapy, leg elevation, exercise). However, only compression therapy has some evidence to support its effectiveness in prevention and problems with adherence to this strategy are well documented. Aim The aim of this research was to identify factors associated with recurrence by determining relationships between recurrence and demographic factors, health, physical activity, psychosocial factors and self-care activities to prevent recurrence. Methods Two studies were undertaken: a retrospective study of participants diagnosed with a venous leg ulcer which healed 12 to 36 months prior to the study (n=122); and a prospective longitudinal study of participants recruited as their ulcer healed and data collected for 12 months following healing (n=80). Data were collected from medical records on demographics, medical history and ulcer history and treatments; and from self-report questionnaires on physical activity, nutrition, psychosocial measures, ulcer history, compression and other self-care activities. Follow-up data for the prospective study were collected every three months for 12 months after healing. For the retrospective study, a logistic regression model determined the independent influences of variables on recurrence. For the prospective study, median time to recurrence was calculated using the Kaplan-Meier method and a Cox proportional-hazards regression model was used to adjust for potential confounders and determine effects of preventive strategies and psychosocial factors on recurrence. Results In total, 68% of participants in the retrospective study and 44% of participants in the prospective study suffered a recurrence. After mutual adjustment for all variables in multivariable regression models, leg elevation, compression therapy, self efficacy and physical activity were found to be consistently related to recurrence in both studies. In the retrospective study, leg elevation, wearing Class 2 or 3 compression hosiery, the level of physical activity, cardiac disease and self efficacy scores remained significantly associated (p<0.05) with recurrence. The model was significant (p <0.001); with a R2 equivalent of 0.62. Examination of relationships between psychosocial factors and adherence to wearing compression hosiery found wearing compression hosiery was significantly positively associated with participants’ knowledge of the cause of their condition (p=0.002), higher self-efficacy scores (p=0.026) and lower depression scores (p=0.009). Analysis of data from the prospective study found there were 35 recurrences (44%) in the 12 months following healing and median time to recurrence was 27 weeks. After adjustment for potential confounders, a Cox proportional hazards regression model found that at least an hour/day of leg elevation, six or more days/week in Class 2 (20–25mmHg) or 3 (30–40mmHg) compression hosiery, higher social support scale scores and higher General Self-Efficacy scores remained significantly associated (p<0.05) with a lower risk of recurrence, while male gender and a history of DVT remained significant risk factors for recurrence. Overall the model was significant (p <0.001); with an R2 equivalent 0.72. Conclusions The high rates of recurrence found in the studies highlight the urgent need for further information in this area to support development of effective strategies for prevention. Overall, results indicate leg elevation, physical activity, compression hosiery and strategies to improve self-efficacy are likely to prevent recurrence. In addition, optimal management of depression and strategies to improve patient knowledge and self-efficacy may positively influence adherence to compression therapy. This research provides important information for development of strategies to prevent recurrence of venous leg ulcers, with the potential to improve health and decrease health care costs in this population.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

Description of plant tRNA-derived RNA fragments (tRFs) associated with argonaute and identification of their putative targets

tRNA-derived RNA fragments (tRFs) are 19mer small RNAs that associate with Argonaute (AGO) proteins in humans. However, in plants, it is unknown if tRFs bind with AGO proteins. Here, using public deep sequencing libraries of immunoprecipitated Argonaute proteins (AGO-IP) and bioinformatics approaches, we identified the Arabidopsis thaliana AGO-IP tRFs. Moreover, using three degradome deep sequencing libraries, we identified four putative tRF targets. The expression pattern of tRFs, based on deep sequencing data, was also analyzed under abiotic and biotic stresses. The results obtained here represent a useful starting point for future studies on tRFs in plants. © 2013 Loss-Morais et al.; licensee BioMed Central Ltd.

Identification of reduced models of power system areas using phasor measurement units

In this paper an approach is presented for identification of a reduced model for coherent areas in power systems using phasor measurement units to represent the inter-area oscillations of the system. The generators which are coherent in a wide range of operating conditions form the areas in power systems and the reduced model is obtained by representing each area by an equivalent machine. The reduced nonlinear model is then identified based on the data obtained from measurement units. The simulation is performed on three test systems and the obtained results show high accuracy of identification process.

Practical aspects of frequency-domain identification of dynamic models of marine structures from hydrodynamic data

The motion response of marine structures in waves can be studied using finite-dimensional linear-time-invariant approximating models. These models, obtained using system identification with data computed by hydrodynamic codes, find application in offshore training simulators, hardware-in-the-loop simulators for positioning control testing, and also in initial designs of wave-energy conversion devices. Different proposals have appeared in the literature to address the identification problem in both time and frequency domains, and recent work has highlighted the superiority of the frequency-domain methods. This paper summarises practical frequency-domain estimation algorithms that use constraints on model structure and parameters to refine the search of approximating parametric models. Practical issues associated with the identification are discussed, including the influence of radiation model accuracy in force-to-motion models, which are usually the ultimate modelling objective. The illustration examples in the paper are obtained using a freely available MATLAB toolbox developed by the authors, which implements the estimation algorithms described.