Women’s Wellness after Cancer Program Study (WWACP)– A multi centre randomised controlled trial examining the benefits and cost effectiveness of an Evidenced Based, e-health intervention

Background Advances in cancer diagnosis and treatment have significantly improved survival rates, through their subsequent health needs are often not adequately addressed by current health services. National Health and Medical Research Council (NHMRC) Partnerships Project awarded a national collaborative project to develop, trial and evaluate clinical benefits and cost effectiveness of an e-health enabled structured health promotion intervention - The Women’s Wellness after Cancer Program (WWACP). The aim of this e-health enabled multimodal intervention is to improve health related quality of life in women previously treated for target cancers. Aim The WWACP is a 12-week web based, interactive, holistic program. Primary outcomes for this project are to promote a positive change in health-related quality of life (HRQoL) and reduction in Body Mass Index (BMI) in the women undertaking WWACP compared to women who receive usual care. Secondary outcomes include managing other side effects of cancer treatment through evidence-based nutrition and exercise practices, dealing with stress, sleep, menopause and sexuality issues. Methods The single-blinded multi-center randomized controlled trial recruited a toatl of 330 women within 24 months of completion of chemotherapy and /or radiotherapy. Women were randomly assigned to either a usual care or intervention group. Women provided with the intervention were provided with an interactive iBook and journal, web interface, and three virtual consultations by experienced cancer nurses. A variety of methods were utilized, to enable positive self- efficacy and lifestyle changes. These include online coaching with a registered nurse trained in the intervention, plus written educational and health promotional information. The program has been delivered through the e-health enabled interfaces, which enables virtual delivery via desktop and mobile computing devices. Importantly this enables accessibility for rural and regional women in Australia who are frequently geographically disadvantaged in terms of health care provision. Results Research focusing on alternative methods of delivering post treatment / or survivorship care in cancer utilizing web based interfaces is limited, but emerging evidence suggests that Internet interventions can increase psychological and physical wellbeing in cancer patients. The WWACP trial aims to establish the effectiveness of delivery of the program in terms of positive patient outcomes and cost effective, flexibility. The trial will be completed in September and results will be presented at the conference. Conclusions Women after acute hematological, breast and gynecological cancer treatments demonstrate good cancer survival rates and face residual health problems which are amenable to behavioral interventions. The conclusion of active treatment is a key 'teachable moment' in which sustainable positive lifestyle change can be achieved if patients receive education and psychological support which targets key treatment related health problems and known chronic disease risk factors.

Detection of multiple source locations using a glowworm metaphor with applications to collective robotics

This paper presents a glowworm swarm based algorithm that finds solutions to optimization of multiple optima continuous functions. The algorithm is a variant of a well known ant-colony optimization (ACO) technique, but with several significant modifications. Similar to how each moving region in the ACO technique is associated with a pheromone value, the agents in our algorithm carry a luminescence quantity along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luminescence and have a circular sensor range. The glowworms depend on a local-decision domain to compute their movements. Simulations demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a multimodal function. The above optimization scenario solves problems where a collection of autonomous robots is used to form a mobile sensor network. In particular, we address the problem of detecting multiple sources of a general nutrient profile that is distributed spatially on a two dimensional workspace using multiple robots.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources

This paper presents a glowworm metaphor based distributed algorithm that enables a collection of minimalist mobile robots to split into subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple radiation sources such as nuclear/hazardous chemical spills and fire-origins in a fire calamity. The algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the glowworm algorithm carry a luminescence quantity called luciferin along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luciferin. The key feature that is responsible for the working of the algorithm is the use of an adaptive local-decision domain, which we use effectively to detect the multiple source locations of interest. The glowworms have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. Extensive simulations validate the feasibility of applying the glowworm algorithm to the problem of multiple source localization. We build four wheeled robots called glowworms to conduct our experiments. We use a preliminary experiment to demonstrate the basic behavioral primitives that enable each glowworm to exhibit taxis behavior towards source locations and later demonstrate a sound localization task using a set of four glowworms.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Occupational chronic solvent encephalopathy in Finland 1995 - 2007 : incidence and diagnostic methods

The occurrence of occupational chronic solvent encephalopathy (CSE) seems to decrease, but still every year reveals new cases. To prevent CSE and early retirement of solvent-exposed workers, actions should focus on early CSE detection and diagnosis. Identifying the work tasks and solvent exposure associated with high risk for CSE is crucial. Clinical and exposure data of all the 128 cases diagnosed with CSE as an occupational disease in Finland during 1995-2007 was collected from the patient records at the Finnish Institute of Occupational Health (FIOH) in Helsinki. The data on the number of exposed workers in Finland were gathered from the Finnish Job-exposure Matrix (FINJEM) and the number of employed from the national workforce survey. We analyzed the work tasks and solvent exposure of CSE patients and the findings in brain magnetic resonance imaging (MRI), quantitative electroencephalography (QEEG), and event-related potentials (ERP). The annual number of new cases diminished from 18 to 3, and the incidence of CSE decreased from 8.6 to 1.2 / million employed per year. The highest incidence of CSE was in workers with their main exposure to aromatic hydrocarbons; during 1995-2006 the incidence decreased from 1.2 to 0.3 / 1 000 exposed workers per year. The work tasks with the highest incidence of CSE were floor layers and lacquerers, wooden surface finishers, and industrial, metal, or car painters. Among 71 CSE patients, brain MRI revealed atrophy or white matter hyperintensities or both in 38% of the cases. Atrophy which was associated with duration of exposure was most frequently located in the cerebellum and in the frontal or parietal brain areas. QEEG in a group of 47 patients revealed increased power of the theta band in the frontal brain area. In a group of 86 patients, the P300 amplitude of auditory ERP was decreased, but at individual level, all the amplitude values were classified as normal. In 11 CSE patients and 13 age-matched controls, ERP elicited by a multimodal paradigm including an auditory, a visual detection, and a recognition memory task under single and dual-task conditions corroborated the decrease of auditory P300 amplitude in CSE patients in single-task condition. In dual-task conditions, the auditory P300 component was, more often in patients than in controls, unrecognizable. Due to the paucity and non-specificity of the findings, brain MRI serves mainly for differential diagnostics in CSE. QEEG and auditory P300 are insensitive at individual level and not useful in the clinical diagnostics of CSE. A multimodal ERP paradigm may, however, provide a more sensitive method to diagnose slight cognitive disturbances such as CSE.

An ensemble of B-DNA dinucleotide geometries lead to characteristic nucleosomal DNA structure and provide plasticity required for gene expression

Background: A nucleosome is the fundamental repeating unit of the eukaryotic chromosome. It has been shown that the positioning of a majority of nucleosomes is primarily controlled by factors other than the intrinsic preference of the DNA sequence. One of the key questions in this context is the role, if any, that can be played by the variability of nucleosomal DNA structure. Results: In this study, we have addressed this question by analysing the variability at the dinucleotide and trinucleotide as well as longer length scales in a dataset of nucleosome X-ray crystal structures. We observe that the nucleosome structure displays remarkable local level structural versatility within the B-DNA family. The nucleosomal DNA also incorporates a large number of kinks. Conclusions: Based on our results, we propose that the local and global level versatility of B-DNA structure may be a significant factor modulating the formation of nucleosomes in the vicinity of high-plasticity genes, and in varying the probability of binding by regulatory proteins. Hence, these factors should be incorporated in the prediction algorithms and there may not be a unique `template' for predicting putative nucleosome sequences. In addition, the multimodal distribution of dinucleotide parameters for some steps and the presence of a large number of kinks in the nucleosomal DNA structure indicate that the linear elastic model, used by several algorithms to predict the energetic cost of nucleosome formation, may lead to incorrect results.

``Clickable'', Trifunctional Magnetite Nanoparticles and Their Chemoselective Biofunctionalization

A multifunctional iron oxide based nanoformulation for combined cancer-targeted therapy and multimodal imaging has been meticulously designed and synthesized using a chemoselective ligation approach. Novel superparamagnetic magnetite nanoparticles simultaneously functionalized with amine, carboxyl, and azide groups were fabricated through a sequence of stoichiometrically controllable partial succinylation and Cu (II) catalyzed diazo transfer on the reactive amine termini of 2-aminoethylphosphonate grafted magnetite nanoparticles (MNPs). Functional moieties associated with MNP surface were chemoselectively conjugated with rhodamine B isothiocyanate (RITC), propargyl folate (FA), and paclitaxel (PTX) via tandem nucleophic addition of amine to isothithiocyanates, Cu (I) catalyzed azide-alkyne click chemistry and carbodiimide-promoted esterification. An extensive in vitro study established that the bioactives chemoselectively appended to the magnetite core bequeathed multifunctionality to the nanoparticles without any loss of activity of the functional molecules. Multifunctional nanoparticles, developed in the course of the study, could selectively target and induce apoptosis to folate-receptor (FR) overexpressing cancer cells with enhanced efficacy as compared to the free drug. In addition, the dual optical and magnetic properties of the synthesized nanoparticles aided in the real-time tracking of their intracellular pathways also as apoptotic events through dual fluorescence and MR-based imaging.

Development of microstructure and texture in Copper during warm accumulative roll bonding

The evolution of microstructure and texture gradient in warm Accumulative Roll Bonded Cu-Cu multilayer has been studied. Grain size distribution is multimodal and exhibits variation from middle to surface layer. Evolution of texture is largely influenced by shear, in addition to rolling deformation. This leads to the formation of a texture comprising of high fraction of Brass and rolling direction-rotated cube components. Partial recrystallization was observed. Deformed and recrystallized grains were separated using a partition scheme based on grain orientation spread and textures were analyzed for both the partition. Retention of deformation texture components in recrystallized grains suggests the mechanism of recrystallization as continuous recrystallization. Shear deformation plays an important role in grain refinement through continuous recrystallization. (C) 2012 Elsevier Inc. All rights reserved.

Prior image-constrained l(1)-norm-based reconstruction method for effective usage of structural information in diffuse optical tomography

A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in multimodal diffuse optical tomographic imaging is introduced. This approach is based on a prior image-constrained-l(1) minimization scheme and has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the proposed framework is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information. (C) 2012 Optical Society of America

Speeded up Gaussian mixture model algorithm for background subtraction

Adaptive Gaussian Mixture Models (GMM) have been one of the most popular and successful approaches to perform foreground segmentation on multimodal background scenes. However, the good accuracy of the GMM algorithm comes at a high computational cost. An improved GMM technique was proposed by Zivkovic to reduce computational cost by minimizing the number of modes adaptively. In this paper, we propose a modification to his adaptive GMM algorithm that further reduces execution time by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we derive a heuristic that computes periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Modified roaming optimization for multi-modal optima

This paper explains the algorithm of Modified Roaming Optimization (MRO) for capturing the multiple optima for multimodal functions. There are some similarities between the Roaming Optimization (RO) and MRO algorithms, but the MRO algorithm is created to overcome the problems facing while applying the RO to the problems possessing large number of solutions. The MRO mainly uses the concept of density to overcome the challenges posed by RO. The algorithm is tested with standard test functions and also discussions are made to improve the efficacy of the MRO algorithm. This paper also gives the results of MRO applied for solving Inverse Kinematics (IK) problem for SCARA and PUMA robots.

Spectral response of a droplet in pulsating external flow field

A droplet introduced in an external convective flow field exhibits significant multimodal shape oscillations depending upon the intensity of the aerodynamic forcing. In this paper, a theoretical model describing the temporal evolution of normal modes of the droplet shape is developed. The fluid is assumed to be weakly viscous and Newtonian. The convective flow velocity, which is assumed to be incompressible and inviscid, is incorporated in the model through the normal stress condition at the droplet surface and the equation of motion governing the dynamics of each mode is derived. The coupling between the external flow and the droplet is approximated to be a one-way process, i.e., the external flow perturbations effect the droplet shape oscillations and the droplet oscillation itself does not influence the external flow characteristics. The shape oscillations of the droplet with different fluid properties under different unsteady flow fields were simulated. For a pulsatile external flow, the frequency spectra of the normal modes of the droplet revealed a dominant response at the resonant frequency, in addition to the driving frequency and the corresponding harmonics. At driving frequencies sufficiently different from the resonant frequency of the prolate-oblate oscillation mode of the droplet, the oscillations are stable. But at resonance the oscillation amplitude grows in time leading to breakup depending upon the fluid viscosity. A line vortex advecting past the droplet, simulated as an isotropic jump in the far field velocity, leads to the resonant excitation of the droplet shape modes if and only if the time taken by the vortex to cross the droplet is less than the resonant period of the P-2 mode of the droplet. A train of two vortices interacting with the droplet is also analysed. It shows clearly that the time instant of introduction of the second vortex with respect to the droplet shape oscillation cycle is crucial in determining the amplitude of oscillation. (C) 2014 AIP Publishing LLC.