Single-agent versus combination chemotherapy in patients with advanced non-small cell lung cancer and a performance status of 2 : prognostic factors and treatment selection based on two large randomized clinical trials

Purpose: Data from two randomized phase III trials were analyzed to evaluate prognostic factors and treatment selection in the first-line management of advanced non-small cell lung cancer patients with performance status (PS) 2. Patients and Methods: Patients randomized to combination chemotherapy (carboplatin and paclitaxel) in one trial and single-agent therapy (gemcitabine or vinorelbine) in the second were included in these analyses. Both studies had identical eligibility criteria and were conducted simultaneously. Comparison of efficacy and safety was performed between the two cohorts. A regression analysis identified prognostic factors and subgroups of patients that may benefit from combination or single-agent therapy. Results: Two hundred one patients were treated with combination and 190 with single-agent therapy. Objective responses were 37 and 15%, respectively. Median time to progression was 4.6 months in the combination arm and 3.5 months in the single-agent arm (p < 0.001). Median survival imes were 8.0 and 6.6 months, and 1-year survival rates were 31 and 26%, respectively. Albumin <3.5 g, extrathoracic metastases, lactate dehydrogenase ≥200 IU, and 2 comorbid conditions predicted outcome. Patients with 0-2 risk factors had similar outcomes independent of treatment, whereas patients with 3-4 factors had a nonsignificant improvement in median survival with combination chemotherapy. Conclusion: Our results show that PS2 non-small cell lung cancer patients are a heterogeneous group who have significantly different outcomes. Patients treated with first-line combination chemotherapy had a higher response and longer time to progression, whereas overall survival did not appear significantly different. A prognostic model may be helpful in selecting PS 2 patients for either treatment strategy. © 2009 by the International Association for the Study of Lung Cancer.

Theatre-arts pedagogy for social justice : case study of the area youth foundation in Jamaica

In this paper I describe and analyse the socio-educational significance of a theatre arts approach to learning for young adults in Jamaica, implemented by the Area Youth Foundation (AYF). Briefly outlining the genesis and development of the AYF, I provide snapshots of the experiences and destinations of some of its young participants. The paper discusses AYF workshops to show how the pedagogy was shaped by the expressive arts and based on the critical praxis approach systematized by Paulo Freire in adult education and Augusto Boal in theatre. Based on interviews with AYF’s leader and some of the learners, I discuss how the foundation’s motto, “Youth Empowerment Through the Arts,” is played out in workshops and creative productions that are simultaneously learner-driven and teacher-guided, with the powerful impact of inspiring politically thoughtful creativity and skills in youths from less-privileged communities.

Bayesian inference of traffic volumes based on Bluetooth data

The study of the relationship between macroscopic traffic parameters, such as flow, speed and travel time, is essential to the understanding of the behaviour of freeway and arterial roads. However, the temporal dynamics of these parameters are difficult to model, especially for arterial roads, where the process of traffic change is driven by a variety of variables. The introduction of the Bluetooth technology into the transportation area has proven exceptionally useful for monitoring vehicular traffic, as it allows reliable estimation of travel times and traffic demands. In this work, we propose an approach based on Bayesian networks for analyzing and predicting the complex dynamics of flow or volume, based on travel time observations from Bluetooth sensors. The spatio-temporal relationship between volume and travel time is captured through a first-order transition model, and a univariate Gaussian sensor model. The two models are trained and tested on travel time and volume data, from an arterial link, collected over a period of six days. To reduce the computational costs of the inference tasks, volume is converted into a discrete variable. The discretization process is carried out through a Self-Organizing Map. Preliminary results show that a simple Bayesian network can effectively estimate and predict the complex temporal dynamics of arterial volumes from the travel time data. Not only is the model well suited to produce posterior distributions over single past, current and future states; but it also allows computing the estimations of joint distributions, over sequences of states. Furthermore, the Bayesian network can achieve excellent prediction, even when the stream of travel time observation is partially incomplete.

A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

Serum HE4 as a prognostic marker in endometrial cancer : a population based study

Objective HE4 has emerged as a promising biomarker in gynaecological oncology. The purpose of this study was to evaluate serum HE4 as a biomarker for high-risk phenotypes in a population-based endometrial cancer cohort. Methods Peri-operative serum HE4 and CA125 were measured in 373 patients identified from the prospective Australian National Endometrial Cancer Study (ANECS). HE4 and CA125 were quantified on the ARCHITECT instrument in a clinically accredited laboratory. Receiver operator curves (ROC), Spearman rank correlation coefficient, and chi-squared and Mann–Whitney tests were used for statistical analysis. Survival analysis was performed using Kaplan–Meier and Cox multivariate regression analyses. Results Median CA125 and HE4 levels were higher in stage III and IV tumours (p < 0.001) and in tumours with outer-half myometrial invasion (p < 0.001). ROC analysis demonstrated that HE4 (area under the curve (AUC) = 0.76) was a better predictor of outer-half myometrial invasion than CA125 (AUC = 0.65), particularly in patients with low-grade endometrioid tumours (AUC 0.77 vs 0.64 for CA125). Cox multivariate analysis demonstrated that elevated HE4 was an independent predictor of recurrence-free survival (HR = 2.40, 95% CI 1.19–4.83, p = 0.014) after adjusting for stage and grade of disease, particularly in the endometrioid subtype (HR = 2.86, 95% CI 1.25–6.51, p = 0.012). Conclusion These findings demonstrate the utility of serum HE4 as a prognostic biomarker in endometrial cancer in a large, population-based study. In particular they highlight the utility of HE4 for pre-operative risk stratification to identify high-risk patients within low-grade endometrioid endometrial cancer patients who might benefit from lymphadenectomy.

Creating synergy in teacher education through robotics-based STEM activities

Robotics has created opportunities for educators to teach concepts across Science, Technology, Engineering, and Mathematics (STEM). This is one of the reasons robotics is becoming increasingly common in primary and secondary classrooms in Australia. To enable pre-service teachers to design engaging STEM activities that incorporate these technologies, robotics is part of the teaching program in the primary education degree at Queensland University of Technology (QUT). A number of pre-service teachers also choose to extend their abilities by implementing robotics activities on field studies, in schools on a voluntary basis, and in outreach activities such as the Robotics@QUT project. The Robotics@QUT project is a support network developed to build professional knowledge and capacity of classroom teachers in schools from a low SES area, engaging in robotics-based STEM activities. Professional Development (PD) workshops are provided to teachers in order to build their knowledge and confidence in implementing robotics activities in their classrooms, loan kits are provided, and pre-service teacher visits arranged to provide the teachers with on-going support. A key feature of the project is the partnerships developed between the teachers and the pre-service teachers involved in the project. The purpose of this study was to ascertain how the teachers in the project perceived the value of the PD workshops and the pre-service teachers’ involvement and what the benefits of the involvement in the project were for the pre-service teachers. Seventeen teachers completed a five-point (1-5) likert scale questionnaire regarding their involvement in the Robotics@QUT project. Teachers’ responses on the value of the project and the pre-service teacher support highlighted the benefits of the partnerships formed and provided insights into the value of the support provided by the pre-service teachers. This paper also describes one pre-service teacher’s experience with the project and the perceived benefits from being involved.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Wide-area control of aggregated power systems

This paper proposes a new method for stabilizing disturbed power systems using wide area measurement and FACTS devices. The approach focuses on both first swing and damping stability of power systems following large disturbances. A two step control algorithm based on Lyapunov Theorem is proposed to be applied on the controllers to improve the power systems stability. The proposed approach is simulated on two test systems and the results show significant improvement in the first swing and damping stability of the test systems.

Novelty-based visual obstacle detection in agriculture

This paper describes a novel obstacle detection system for autonomous robots in agricultural field environments that uses a novelty detector to inform stereo matching. Stereo vision alone erroneously detects obstacles in environments with ambiguous appearance and ground plane such as in broad-acre crop fields with harvested crop residue. The novelty detector estimates the probability density in image descriptor space and incorporates image-space positional understanding to identify potential regions for obstacle detection using dense stereo matching. The results demonstrate that the system is able to detect obstacles typical to a farm at day and night. This system was successfully used as the sole means of obstacle detection for an autonomous robot performing a long term two hour coverage task travelling 8.5 km.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Efficient fuzzy matching and intersection on private datasets

At Eurocrypt’04, Freedman, Nissim and Pinkas introduced a fuzzy private matching problem. The problem is defined as follows. Given two parties, each of them having a set of vectors where each vector has T integer components, the fuzzy private matching is to securely test if each vector of one set matches any vector of another set for at least t components where t < T. In the conclusion of their paper, they asked whether it was possible to design a fuzzy private matching protocol without incurring a communication complexity with the factor (T t ) . We answer their question in the affirmative by presenting a protocol based on homomorphic encryption, combined with the novel notion of a share-hiding error-correcting secret sharing scheme, which we show how to implement with efficient decoding using interleaved Reed-Solomon codes. This scheme may be of independent interest. Our protocol is provably secure against passive adversaries, and has better efficiency than previous protocols for certain parameter values.

Heterogeneity of clinical and environmental isolates of Mycobacterium fortuitum using repetitive element sequence-based PCR: municipal water an unlikely source of community-acquired infections

M. fortuitum is a rapidly growing mycobacterium associated with community-acquired and nosocomial wound, soft tissue, and pulmonary infections. It has been postulated that water has been the source of infection especially in the hospital setting. The aim of this study was to determine if municipal water may be the source of community-acquired or nosocomial infections in the Brisbane area. Between 2007 and 2009, 20 strains of M. fortuitum were recovered from municipal water and 53 patients’ isolates were submitted to the reference laboratory. A wide variation in strain types was identified using repetitive element sequence-based PCR, with 13 clusters of ≥2 indistinguishable isolates, and 28 patterns consisting of individual isolates. The clusters could be grouped into seven similar groups (>95% similarity). Municipal water and clinical isolates collected during the same time period and from the same geographical area consisted of different strain types, making municipal water an unlikely source of sporadic human infection.

Urban traffic state estimation: Fusing point and zone based data

Loop detectors are the oldest and widely used traffic data source. On urban arterials, they are mainly installed for signal control. Recently state of the art Bluetooth MAC Scanners (BMS) has significantly captured the interest of stakeholders for exploiting it for area wide traffic monitoring. Loop detectors provide flow- a fundamental traffic parameter; whereas BMS provides individual vehicle travel time between BMS stations. Hence, these two data sources complement each other, and if integrated should increase the accuracy and reliability of the traffic state estimation. This paper proposed a model that integrates loops and BMS data for seamless travel time and density estimation for urban signalised network. The proposed model is validated using both real and simulated data and the results indicate that the accuracy of the proposed model is over 90%.

Bone regeneration based on tissue engineering conceptions – a 21st century perspective

The role of Bone Tissue Engineering in the field of Regenerative Medicine has been the topic of substantial research over the past two decades. Technological advances have improved orthopaedic implants and surgical techniques for bone reconstruction. However, improvements in surgical techniques to reconstruct bone have been limited by the paucity of autologous materials available and donor site morbidity. Recent advances in the development of biomaterials have provided attractive alternatives to bone grafting expanding the surgical options for restoring the form and function of injured bone. Specifically, novel bioactive (second generation) biomaterials have been developed that are characterised by controlled action and reaction to the host tissue environment, whilst exhibiting controlled chemical breakdown and resorption with an ultimate replacement by regenerating tissue. Future generations of biomaterials (third generation) are designed to be not only osteo- conductive but also osteoinductive, i.e. to stimulate regeneration of host tissues by combining tissue engineer- ing and in situ tissue regeneration methods with a focus on novel applications. These techniques will lead to novel possibilities for tissue regeneration and repair. At present, tissue engineered constructs that may find future use as bone grafts for complex skeletal defects, whether from post-traumatic, degenerative, neoplastic or congenital/developmental “origin” require osseous reconstruction to ensure structural and functional integrity. Engineering functional bone using combinations of cells, scaffolds and bioactive factors is a promising strategy and a particular feature for future development in the area of hybrid materials which are able to exhibit suitable biomimetic and mechanical properties. This review will discuss the state of the art in this field and what we can expect from future generations of bone regeneration concepts.

Energy-based positioning control of underactuated vehicles using manifold regulation

In this paper, we consider the problem of position regulation of a class of underactuated rigid-body vehicles that operate within a gravitational field and have fully-actuated attitude. The control objective is to regulate the vehicle position to a manifold of dimension equal to the underactuation degree. We address the problem using Port-Hamiltonian theory, and reduce the associated matching PDEs to a set of algebraic equations using a kinematic identity. The resulting method for control design is constructive. The point within the manifold to which the position is regulated is determined by the action of the potential field and the geometry of the manifold. We illustrate the performance of the controller for an unmanned aerial vehicle with underactuation degree two-a quadrotor helicopter.